SAA-C03 Exam Dumps - AWS Certified Solutions Architect - Associate (SAA-C03)

We recommend that you use On-Demand Instances for applications with short-term, irregular workloads that cannot be interrupted. https://docs.aws.amazon.co m/AWSEC2/latest/UserGuide/ec2-on-demand-instances.html

This solution meets the requirements most cost-effectively because it leverages the serverless and event-driven capabilities of AWS Lambda and Amazon EventBridge. AWS Lambda allows you to run code without provisioning or managing servers, and you pay only for the compute time you consume. Amazon EventBridge is a serverless event bus service that lets you connect your applications with data from various sources and routes that data to targets such as AWS Lambda. By using Amazon EventBridge, you can create a rule that triggers a Lambda function to run the program when reports are requested, and you can also schedule the rule to run during the last week of each month. This way, you can generate reports in the least amount of time and pay only for the resources you use.

References:

AWS Lambda

Amazon EventBridge

This solution meets the following requirements:

It is cost-effective, as it only charges for the storage and data transfer of the replicated objects, and does not require any additional AWS services or custom scripts. S3 Same-Region Replication (SRR) is a feature that automatically replicates objects across S3 buckets within the same AWS Region. SRR can help you aggregate logs from multiple sources to a single destination for analysis and auditing. SRR also preserves the metadata, encryption, and access control of the source objects.

It is operationally efficient, as it does not require any manual intervention or scheduling. SRR replicates objects as soon as they are uploaded to the source bucket, ensuring that the destination bucket always has the latest log data. SRR also handles any updates or deletions of the source objects, keeping the destination bucket in sync. SRR can be enabled with a few clicks in the S3 console or with a simple API call.

It is secure, as it does not allow the logs to leave the us-west-2 Region. SRR only replicates objects within the same AWS Region, ensuring that the data sovereignty and compliance requirements are met. SRR also supports encryption of the source and destination objects, using either server-side encryption with AWS KMS or S3-managed keys, or client-side encryption.

References:

Same-Region Replication - Amazon Simple Storage Service

How do I replicate objects across S3 buckets in the same AWS Region?

Centralized Logging on AWS | AWS Solutions | AWS Solutions Library

AWS Control Tower is a fully managed service that simplifies the setup and governance of a secure, compliant, multi-account AWS environment. It establishes a landing zone that is based on best-practices blueprints, and it enables governance using controls you can choose from a pre-packaged list. The landing zone is a well-architected, multi-account baseline that follows AWS best practices. Controls implement governance rules for security, compliance, and operations. AWS Security Hub is a service that provides a comprehensive view of your security posture across your AWS accounts. It aggregates, organizes, and prioritizes security alerts and findings from multiple AWS services, such as Amazon GuardDuty, Amazon Inspector, Amazon Macie, AWS Firewall Manager, and AWS IAM Access Analyzer, as well as from AWS Partner solutions. AWS Security Hub continuously monitors your environment using automated compliance checks based on the AWS best practices and industry standards, such as the AWS Foundational Security Best Practices (FSBP) standard. AWS Control Tower Account Factory is a feature that automates the provisioning of new AWS accounts that are preconfigured to meet your business, security, and compliance requirements. By deploying an AWS Control Tower environment in the Organizations management account, you can leverage the existing organization structure and policies, and enable AWS Security Hub and AWS Control Tower Account Factory in the environment. This way, you can audit all API calls and logins in any existing or new AWS account, monitor the compliance status of each account with the FSBP standard, and provision new accounts with ease and consistency. This solution meets the requirements with the least operational overhead, as you do not need to manage any infrastructure, perform any data migration, or submit any requests for changes.

References:

AWS Control Tower

[AWS Security Hub]

[AWS Control Tower Account Factory]

The most suitable solution for the company’s requirements is to configure AWS Glue DataBrew to transform the data and share the transformation steps with employees by using DataBrew recipes. This solution will provide a prebuilt solution for data transformation that does not require code, and will also provide data lineage and data profiling. The company can easily share the data transformation steps with employees throughout the company by using DataBrew recipes.

AWS Glue DataBrew is a visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data for analytics or machine learning by up to 80% faster. Users can upload their data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon Aurora, or Glue Data Catalog, and use a point-and-click interface to apply over 250 built-in transformations. Users can also preview the results of each transformation step and see how it affects the quality and distribution of the data1.

A DataBrew recipe is a reusable set of transformation steps that can be applied to one or more datasets. Users can create recipes from scratch or use existing ones from the DataBrew recipe library. Users can also export, import, or share recipes with other users or groups within their AWS account or organization2.

DataBrew also provides data lineage and data profiling features that help users understand and improve their data quality. Data lineage shows the source and destination of each dataset and how it is transformed by each recipe step. Data profiling shows various statistics and metrics about each dataset, such as column

This solution meets the following requirements:

It is operationally efficient, as it only requires one activation of the cost allocation tag and one creation of the cost report from the management account, which has access to all the member accounts’ data and billing preferences.

It is consistent, as it uses the AWS-defined cost allocation tag named department, which is automatically applied to resources when the company creates tags using the tagging policy enforced by AWS Organizations. This ensures that the tag name and value are the same across all the resources and accounts, and avoids any discrepancies or errors that might arise from user-defined tags.

It is informative, as it creates one cost report in Cost Explorer grouping by the tag name, and filters by EC2. This allows the accounting team to see the breakdown of EC2 consumption and costs by department, regardless of the AWS account. The team can also use other features of Cost Explorer, such as charts, filters, and forecasts, to analyze and optimize the spending.

References:

Using AWS cost allocation tags - AWS Billing

User-defined cost allocation tags - AWS Billing

Cost Tagging and Reporting with AWS Organizations

This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefit from faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.

References:

Predictive scaling for Amazon EC2 Auto Scaling

[Target tracking scaling policies for Amazon EC2 Auto Scaling]

To predict the resources needed for manufacturing processes each month using historical values that are currently stored in an Amazon S3 bucket, a solutions architect should use Amazon SageMaker to train a model by using the historical data in the S3 bucket, and deploy an Amazon SageMaker model and create a SageMaker endpoint for inference. Amazon SageMaker is a fully managed service that provides an easy way to build, train, and deploy machine learning (ML) models. The solutions architect can use the built-in algorithms or frameworks provided by SageMaker, or bring their own custom code, to train a model using the historical data in the S3 bucket as input. The trained model can then be deployed to a SageMaker endpoint, which is a scalable and secure web service that can handle requests for predictions from the application. The solutions architect does not need to have any ML experience or manage any infrastructure to use SageMaker.

 S3 Lifecycle is a feature that allows you to automate the management of your S3 objects based on predefined rules. You can use S3 Lifecycle to delete expired object versions and retain the two most recent versions by creating a lifecycle configuration rule that applies to all objects in the bucket and specifies the expiration action for noncurrent versions. This way, you can reduce the storage costs of your S3 bucket without requiring any application changes or manual intervention. S3 Lifecycle runs once a day and marks the eligible object versions for deletion. You are no longer charged for the objects that are marked for deletion. S3 Lifecycle is the most cost-effective and simple solution among the options.

B. Use an AWS Lambda function to check for older versions and delete all but the two most recent versions. This option is not optimal because it requires you to write, test, and maintain a custom Lambda function that scans the S3 bucket for older versions and deletes them. This can incur additional costs for Lambda invocations and increase the operational complexity and overhead. Moreover, you need to ensure that your Lambda function has the appropriate permissions and error handling mechanisms to perform the deletion operation.

C. Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions. This option is not ideal because S3 Batch Operations is designed for performing large-scale operations on S3 objects, such as copying, tagging, restoring, or invoking a Lambda function. To use S3 Batch Operations to delete noncurrent object versions, you need to provide a manifest file that lists the object versions that you want to delete. This can be challenging and time-consuming to generate and update. Moreover, S3 Batch Operations charges you for each operation that you perform, which can increase your costs.

D. Deactivate versioning on the S3 bucket and retain the two most recent versions. This option is not feasible because deactivating versioning on an S3 bucket does not delete the existing object versions. Instead, it prevents new versions from being created. Therefore, you still need to delete the older versions manually or use another method to do so. Additionally, deactivating versioning can compromise the data protection and recovery capabilities of your S3 bucket.

References:

1 Considering four different replication options for data in Amazon S3 | AWS Storage Blog

2 Using AWS Lambda with Amazon S3 batch operations - AWS Lambda

3 Empty an Amazon S3 bucket with a lifecycle configuration rule

4 Amazon S3 - Lifecycle Management - GeeksforGeeks

Amazon EFS is a fully managed, elastic, and scalable file system that can be shared between multiple containers. It provides high availability and fault tolerance by replicating data across multiple Availability Zones. Amazon EFS is compatible with Amazon EKS and AWS Fargate, and can be registered in a StorageClass object on an EKS cluster. Amazon EBS volumes are not supported by AWS Fargate, and cannot be shared between multiple containers without using EBS Multi-Attach, which has limitations and performance implications. EBS Multi-Attach also requires the volumes to be in the same Availability Zone as the worker nodes, which reduces availability and fault tolerance. Synchronizing data between multiple EFS file systems using AWS Lambda is unnecessary, complex, and prone to errors. References:

Amazon EFS Storage Classes

Amazon EKS Storage Classes

Amazon EBS Multi-Attach

The most suitable design change for the company’s application is to request strongly consistent reads for the table. This change will ensure that the requests to the table return the latest data, reflecting the updates from all prior write operations.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB supports two types of read consistency: eventually consistent reads and strongly consistent reads. By default, DynamoDB uses eventually consistent reads, unless users specify otherwise1.

Eventually consistent reads are reads that may not reflect the results of a recently completed write operation. The response might not include the changes because of the latency of propagating the data to all replicas. If users repeat their read request after a short time, the response should return the updated data. Eventually consistent reads are suitable for applications that do not require up-to-date data or can tolerate eventual consistency1.

Strongly consistent reads are reads that return a result that reflects all writes that received a successful response prior to the read. Users can request a strongly consistent read by setting the ConsistentRead parameter to true in their read operations, such as GetItem, Query, or Scan. Strongly consistent reads are suitable for applications that require up-to-date data or cannot tolerate eventual consistency1.

The other options are not correct because they do not address the issue of read consistency or are not relevant for the use case. Adding read replicas to the table is not correct because this option is not supported by DynamoDB. Read replicas are copies of a primary database instance that can serve read-only traffic and improve availability and performance. Read replicas are available for some relational database services, such as Amazon RDS or Amazon Aurora, but not for DynamoDB2. Using a global secondary index (GSI) is not correct because this option is not related to read consistency. A GSI is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI allows users to query the data in different ways, with eventual consistency3. Requesting eventually consistent reads for the table is not correct because this option is already the default behavior of DynamoDB and does not solve the problem of requests not returning the latest data.

References:

Read consistency - Amazon DynamoDB

Working with read replicas - Amazon Relational Database Service

Working with global secondary indexes - Amazon DynamoDB

The most cost-effective solution for the online video game company is to configure a Network Load Balancer with the required protocol and ports for the internet traffic and specify the EC2 instances as the targets. This solution will enable the company to handle millions of UDP requests per second with ultra-low latency and high performance.

A Network Load Balancer is a type of Elastic Load Balancing that operates at the connection level (Layer 4) and routes traffic to targets (EC2 instances, microservices, or containers) within Amazon VPC based on IP protocol data. A Network Load Balancer is ideal for load balancing of both TCP and UDP traffic, as it is capable of handling millions of requests per second while maintaining high throughput at ultra-low latency. A Network Load Balancer also preserves the source IP address of the clients to the back-end applications, which can be useful for logging or security purposes1.

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.

References:

1 Spot Instances - Amazon Elastic Compute Cloud

2 Launch templates - Amazon Elastic Compute Cloud

3 Auto Scaling groups - Amazon EC2 Auto Scaling

[4] Savings Plans - Amazon EC2 Reserved Instances and Other AWS Reservation Models

AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and AWS storage services. AWS DataSync can transfer data at speeds up to 10 times faster than open-source tools by using a purpose-built network protocol and parallelizing data transfers. AWS DataSync also handles encryption, data integrity verification, and bandwidth optimization. To use AWS DataSync, users need to deploy a DataSync agent on their on-premises servers, which connects to the NFS servers and syncs the data to Amazon S3. Users can schedule periodic or one-time sync tasks and monitor the progress and status of the transfers.

The other options are not correct because they are either not cost-effective or not suitable for the use case. Setting up AWS Glue to copy the data from the on-premises servers to Amazon S3 is not cost-effective because AWS Glue is a serverless data integration service that is mainly used for extract, transform, and load (ETL) operations, not for simple data backup. Setting up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3 is not cost-effective because AWS Transfer for SFTP is a fully managed service that provides secure file transfer using the SFTP protocol, which is more suitable for exchanging data with third parties than for backing up data. Setting up an AWS Direct Connect connection between the on-premises data center and a VPC, and copying the data to Amazon S3 is not cost-effective because AWS Direct Connect is a dedicated network connection between AWS and the on-premises location, which has high upfront costs and requires additional configuration.

References:

AWS DataSync

How AWS DataSync works

AWS DataSync FAQs

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create and test microservices that are written in Python or other supported languages. Lambda scales automatically to handle the number of requests per second. You only pay for the compute time you consume. Lambda also integrates with other AWS services, such as Amazon API Gateway, Amazon S3, Amazon DynamoDB, and Amazon SQS, to enable event-driven architectures. Lambda has minimal infrastructure and operational overhead, as you do not need to manage servers, operating systems, patches, or scaling policies.

The other options are not serverless solutions and require more infrastructure and operational support. They also do not scale automatically to handle the number of requests per second. A Spot Fleet is a collection of EC2 instances that run on spare capacity at low prices. However, Spot Instances can be interrupted by AWS at any time, which can affect the availability and performance of your microservice. AWS Elastic Beanstalk is a service that automates the deployment and management of web applications on EC2 instances. However, you still need to provision, configure, and monitor the underlying EC2 instances and load balancers. Amazon EKS is a service that runs Kubernetes on AWS. However, you still need to create, configure, and manage the EC2 instances that form the Kubernetes cluster and nodes. You also need to install and update the Kubernetes software and tools.

References:

What is AWS Lambda?

Building Lambda functions with Python

Create a layer for a Lambda Python function

AWS Lambda – Function in Python

How do I call my AWS Lambda function from a local python script?

Amazon Aurora Serverless v2 is a cost-effective solution that can automatically scale the database capacity up and down based on the application’s needs. It can handle unpredictable traffic spikes without requiring any provisioning or management of database instances. It is compatible with PostgreSQL and offers high performance, availability, and durability1. References: 1: AWS Ramp-Up Guide: Architect2, page 312: AWS Certified Solutions Architect - Associate exam guide3, page 9.

AWS Glue is a serverless data integration service that can crawl, catalog, and prepare data for analysis. AWS Glue can automatically discover the schema and partitioning of the data stored in Apache Parquet format in S3, and create a table in the AWS Glue Data Catalog. Amazon Athena is a serverless interactive query service that can run SQL queries directly on data in S3, without requiring any data loading or transformation. Athena can use the table metadata from the AWS Glue Data Catalog to query the data in S3. By using AWS Glue and Athena, you can analyze the log files in S3 most cost-effectively, as you only pay for the resources consumed by the crawler and the queries, and you do not need to provision or manage any servers or clusters.

References:

AWS Glue

Amazon Athena

Analyzing Data in S3 using Amazon Athena

Deletion protection is a feature of DynamoDB that prevents accidental deletion of tables. When deletion protection is enabled, you cannot delete a table unless you explicitly disable it first. This adds an extra layer of security and reduces the risk of data loss and operational disruption. Deletion protection is easy to enable and disable using the AWS Management Console, the AWS CLI, or the DynamoDB API. This solution has the least operational overhead, as you do not need to create, manage, or invoke any additional resources or services. References:

Using deletion protection to protect your table

Preventing Accidental Table Deletion in DynamoDB

Amazon DynamoDB now supports table deletion protection

The correct solution is to provision a separate AWS KMS key for each customer and encrypt the data server-side. This way, the company can use the S3 encryption feature to protect the data at rest and delegate the control of the encryption keys to the customers. The customers can then use their own IAM roles to access and decrypt their data. The company employees will not be able to access the data because they are not authorized by the KMS key policies. The other options are incorrect because:

Option A and D are using ACM certificates to encrypt the data client-side. This is not a recommended practice for S3 encryption because it adds complexity and overhead to the encryption process. Moreover, the company will have to manage the certificates and their policies for each customer, which is not scalable and secure.

Option B is using a separate KMS key for each customer, but it is using the S3 bucket policy to control the decryption access. This is not a secure solution because the bucket policy applies to the entire bucket, not to individual objects. Therefore, the customers will be able to access and decrypt each other’s data if they have the permission to list the bucket contents. The bucket policy also overrides the KMS key policy, which means the company employees can access the data if they have the permission to use the KMS key.

References:

S3 encryption

KMS key policies

ACM certificates

Using AD Connector with AWS IAM Identity Center (formerly AWS Single Sign-On) allows the company to leverage its existing on-premises Active Directory for centralized identity management and access control. AD Connector acts as a proxy to the on-premises AD without requiring additional infrastructure or complex setup. This solution integrates seamlessly with AWS, allowing the development team to use their existing AD credentials to access AWS resources across multiple accounts managed by AWS Organizations. The permissions for AWS resources can be managed centrally through IAM Identity Center by configuring permission sets.

This solution provides:

Least operational overhead: AD Connector is fully managed, and IAM Identity Center allows centralized management of permissions across accounts.

Secure access: The solution complies with security policies by using existing AD authentication mechanisms.

Option A (AWS Managed AD): Setting up a fully managed AWS AD and establishing a trust is more complex and involves additional operational overhead.

Option B (IAM Users): Manually managing IAM users and permissions is less scalable and increases operational complexity.

Option D (Cognito): Amazon Cognito is more suited for user-facing applications rather than internal identity management for AWS resources.

AWS References:

AD Connector with IAM Identity Center

AWS IAM Identity Center

This JSON text is an identity-based policy that grants specific permissions. The IAM principals that the solutions architect can attach this policy to are Role and Group. This is because the policy is written in JSON and is an identity-based policy, which can be attached to IAM principals such as users, groups, and roles. Identity-based policies are permissions policies that you attach to IAM identities (users, groups, or roles) and explicitly state what that identity is allowed (or denied) to do1. Identity-based policies are different from resource-based policies, which define the permissions around the specific resource1. Resource-based policies are attached to a resource, such as an Amazon S3 bucket or an Amazon EC2 instance1. Resource-based policies can also specify a principal, which is the entity that is allowed or denied access to the resource1. Organization is not an IAM principal, but a feature of AWS Organizations that allows you to manage multiple AWS accounts centrally2. Amazon ECS resource and Amazon EC2 resource are not IAM principals, but AWS resources that can have resource-based policies attached to them34.

References:

Identity-based policies and resource-based policies

AWS Organizations

Amazon ECS task role

Amazon EC2 instance profile

AWS Storage Gateway is a hybrid cloud storage service that allows you to seamlessly integrate your on-premises applications with AWS cloud storage. Volume Gateway is a type of Storage Gateway that presents cloud-backed iSCSI block storage volumes to your on-premises applications. Volume Gateway operates in either cache mode or stored mode. In cache mode, your primary data is stored in Amazon S3, while retaining your frequently accessed data locally in the cache for low latency access. In stored mode, your primary data is stored locally and your entire dataset is available for low latency access on premises while also asynchronously getting backed up to Amazon S3.

For the pharmaceutical company’s use case, cache mode is the most suitable option, as it meets the following requirements:

It reduces the need to scale the on-premises storage infrastructure, as most of the data is stored in Amazon S3, which is scalable, durable, and cost-effective.

It provides low latency access to the subset of the data that the researchers regularly require, as it is cached locally in the Storage Gateway appliance.

It does not require the entire dataset to be accessed on a daily basis, as it is stored in Amazon S3 and can be retrieved on demand.

It offers flexible data protection and recovery options, as it allows taking point-in-time copies of the volumes using AWS Backup, which are stored in AWS as Amazon EBS snapshots.

Therefore, the solutions architect should recommend deploying an AWS Storage Gateway volume gateway with cached volumes with an Amazon S3 bucket as the target storage and migrating the data to the Storage Gateway appliance.

References:

Volume Gateway | Amazon Web Services

How Volume Gateway works (architecture) - AWS Storage Gateway

AWS Storage Volume Gateway - Cached volumes - Stack Overflow

AWS Glue Crawler: AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and load data for analytics. A Glue crawler can automatically discover new data and schema in Amazon S3, making it easy to keep the data catalog up-to-date.

Crawling the Data:

Set up an AWS Glue crawler to scan the S3 bucket containing the clickstream data.

The crawler will automatically detect the schema and create/update the tables in the AWS Glue Data Catalog.

Amazon Athena:

Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

Once the data catalog is updated by the Glue crawler, use Athena to query the clickstream data directly in S3.

Operational Efficiency: This solution leverages fully managed services, reducing operational overhead. Glue crawlers automate data cataloging, and Athena provides a serverless, pay-per-query model for quick data analysis without the need to set up or manage infrastructure.

References:

AWS Glue

Amazon Athena

To ensure that the shopping cart data is preserved at all times, a solutions architect should configure Amazon ElastiCache for Redis to cache catalog data from Amazon DynamoDB and shopping cart data from the user’s session. This solution has the following benefits:

It offers the lowest possible latency from the application, as ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications1.

It scales with traffic volume, as ElastiCache for Redis supports horizontal scaling by adding more nodes or shards to the cluster, and vertical scaling by changing the node type2.

It is highly available, as ElastiCache for Redis supports replication across multiple Availability Zones and automatic failover in case of a primary node failure3.

It preserves user session data even if the user is disconnected and reconnects, as ElastiCache for Redis can store session data, such as user login information and shopping cart contents, in a persistent and durable manner using snapshots or AOF (append-only file) persistence4.

References:

1: https://aws.amazon.com/elasticache/redis/

2: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Scaling.html

3: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Replication.html

4: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/backups.html

https://aws.amazon.com/premiumsupport/knowledge-center/acm-certificate-expiration/

Amazon Cognito user pools provide a secure and scalable user directory for user authentication and management. User pools support various authentication methods, such as username and password, email and password, phone number and password, and social identity providers. User pools also support multi-factor authentication (MFA), which adds an extra layer of security by requiring users to provide a verification code or a biometric factor in addition to their credentials. User pools can also enable risk-based adaptive authentication, which dynamically adjusts the authentication challenge based on the risk level of the sign-in attempt. For example, if a user tries to sign in from an unfamiliar device or location, the user pool can require a stronger authentication factor, such as SMS or email verification code. This feature helps to protect user accounts from unauthorized access and reduce the friction for legitimate users. User pools can scale up to millions of users and integrate with other AWS services, such as Amazon SNS, Amazon SES, AWS Lambda, and AWS KMS.

Amazon Cognito identity pools provide a way to federate identities from multiple identity providers, such as user pools, social identity providers, and corporate identity providers. Identity pools allow users to access AWS resources with temporary, limited-privilege credentials. Identity pools do not provide user authentication or management features, such as MFA or adaptive authentication. Therefore, option B is not correct.

AWS Identity and Access Management (IAM) is a service that helps to manage access to AWS resources. IAM users are entities that represent people or applications that need to interact with AWS. IAM users can be authenticated with a password or an access key. IAM users can also enable MFA for their own accounts, by using the AllowManageOwnUserMFA action in an IAM policy. However, IAM users are not suitable for user authentication for web or mobile applications, as they are intended for administrative purposes. IAM users also do not support adaptive authentication based on risk factors. Therefore, option C is not correct.

AWS IAM Identity Center (AWS Single Sign-On) is a service that enables users to sign in to multiple AWS accounts and applications with a single set of credentials. AWS SSO supports various identity sources, such as AWS SSO directory, AWS Managed Microsoft AD, and external identity providers. AWS SSO also supports MFA for user authentication, which can be configured in the permission sets that define the level of access for each user. However, AWS SSO does not support adaptive authentication based on risk factors. Therefore, option D is not correct.

References:

Amazon Cognito User Pools

Adding Multi-Factor Authentication (MFA) to a User Pool

Risk-Based Adaptive Authentication

Amazon Cognito Identity Pools

IAM Users

Enabling MFA Devices

AWS Single Sign-On

How AWS SSO Works

This option is the most cost-effective and scalable way to allow the Lambda function in the primary account to access the EFS file system in the secondary account. VPC peering enables private connectivity between two VPCs without requiring gateways, VPN connections, or dedicated network connections. The Lambda function can use the VPC peering connection to mount the EFS file system as a local file system and access the files as needed. The solution does not incur additional data transfer or storage costs, and it leverages the existing EFS file system without duplicating or moving the data.

Option A is not cost-effective because it requires creating a new EFS file system and using AWS DataSync to copy the data from the original EFS file system. This would incur additional storage and data transfer costs, and it would not provide real-time access to the files.

Option C is not scalable because it requires creating a second Lambda function in the secondary account and configuring cross-account permissions to invoke it from the primary account. This would add complexity and latency to the solution, and it would increase the Lambda invocation costs.

Option D is not feasible because Lambda layers are not designed to store large amounts of data or provide file system access. Lambda layers are used to share common code or libraries across multiple Lambda functions. Moving the contents of the EFS file system to a Lambda layer would exceed the size limit of 250 MB for a layer, and it would not allow the Lambda function to read or write files to the layer. References:

What Is VPC Peering?

Using Amazon EFS file systems with AWS Lambda

What Are Lambda Layers?

To migrate the DNS hosting service to a more resilient managed DNS service on AWS, the company should use Amazon Route 53, which is a highly available and scalable cloud DNS web service. Route 53 can host public DNS records for the company’s domain name and provide reliable and secure DNS resolution. To rapidly migrate the DNS hosting service, the company should create a public hosted zone for the domain name in Route 53, which is a container for the domain’s DNS records. Then, the company should import the zone file containing the domain records hosted by the previous provider, which is a text file that defines the DNS records for the domain. This way, the company can quickly transfer the existing DNS records to Route 53 without manually creating them. After importing the zone file, the company should update the domain registrar to use the name servers that Route 53 assigns to the hosted zone. This will ensure that DNS queries for the domain name are routed to Route 53 and resolved by the imported records.

This solution meets the requirements because it uses the following components and features:

AdministratorAccess identity-based policy: This is an AWS managed policy that provides full access to AWS services and resources1. By attaching this policy to the IAM user group, the solutions architect can grant the permissions needed for the designated employees to perform any task in the AWS account.

IAM user group: This is a collection of IAM users that share common permissions2. By creating a user group and adding the five designated employees as members, the solutions architect can simplify the management of permissions and reduce the risk of human errors or inconsistencies.

IAM users: These are identities that represent the designated employees in AWS2. By creating an IAM user for each employee and requiring them to sign in with their own credentials, the solutions architect can enhance the security and accountability of the AWS account.

https://aws.amazon.com/eb s/features/

"Provisioned IOPS volumes are backed by solid-state drives (SSDs) and are the highest performance EBS volumes designed for your critical, I/O intensive database applications.

These volumes are ideal for both IOPS-intensive and throughput-intensive workloads that require extremely low latency."

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html

To design a resilient solution that can improve the recovery time for the system, a solutions architect should recommend creating an Amazon CloudWatch alarm to recover the EC2 instance in case of failure. This solution has the following benefits:

It allows the EC2 instance to be automatically recovered when a system status check failure occurs, such as loss of network connectivity, loss of system power, software issues on the physical host, or hardware issues on the physical host that impact network reachability1.

It preserves the instance ID, private IP addresses, Elastic IP addresses, and all instance metadata of the original instance. A recovered instance is identical to the original instance, except for any data that is in-memory, which is lost during the recovery process1.

It does not require any modification of the application code or the EC2 instance configuration. The solutions architect can create a CloudWatch alarm using the AWS Management Console, the AWS CLI, or the CloudWatch API2.

References:

1: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-recover.html

2: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-recover.html#ec2-instance-recover-create-alarm

https://docs.aws.amazon.com/efs/latest/ug/how-it-works.html#how-it-works-ec2

The most suitable solution for the company’s requirements is to set the home AWS Region in AWS Migration Hub and use AWS Application Discovery Service to collect data about the on-premises servers. This solution will enable the company to gather usage and configuration data of its on-premises servers and workloads, and plan a migration to AWS.

AWS Migration Hub is a service that simplifies and accelerates migration tracking by aggregating migration status information into a single console. Users can view the discovered servers, group them into applications, and track the migration status of each application from the Migration Hub console in their home Region. The home Region is the AWS Region where users store their migration data, regardless of which Regions they migrate into1.

AWS Application Discovery Service is a service that helps users plan their migration to AWS by collecting usage and configuration data about their on-premises servers and databases. Application Discovery Service is integrated with AWS Migration Hub and supports two methods of performing discovery: agentless discovery and agent-based discovery. Agentless discovery can be performed by deploying the Application Discovery Service Agentless Collector through VMware vCenter, which collects static configuration data and utilization data for virtual machines (VMs) and databases. Agent-based discovery can be performed by deploying the AWS Application Discovery Agent on each of the VMs and physical servers, which collects static configuration data, detailed time-series system-performance information, inbound and outbound network connections, and processes that are running2.

The other options are not correct because they do not meet the requirements or are not relevant for the use case. Using the AWS Schema Conversion Tool (AWS SCT) to create the relevant templates and using AWS Trusted Advisor to collect data about the on-premises servers is not correct because this solution is not suitable for collecting usage and configuration data of on-premises servers and workloads. AWS SCT is a tool that helps users convert database schemas and code objects from one database engine to another, such as from Oracle to PostgreSQL3. AWS Trusted Advisor is a service that provides best practice recommendations for cost optimization, performance, security, fault tolerance, and service limits4. Using the AWS Schema Conversion Tool (AWS SCT) to create the relevant templates and using AWS Database Migration Service (AWS DMS) to collect data about the on-premises servers is not correct because this solution is not suitable for collecting usage and configuration data of on-premises servers and workloads. As mentioned above, AWS SCT is a tool that helps users convert database schemas and code objects from one database engine to another. AWS DMS is a service that helps users migrate relational databases, non-relational databases, and other types of data stores to AWS with minimal downtime5.

References:

Home Region - AWS Migration Hub

What is AWS Application Discovery Service? - AWS Application Discovery Service

AWS Schema Conversion Tool - Amazon Web Services

What Is Trusted Advisor? - Trusted Advisor

What Is AWS Database Migration Service? - AWS Database Migration Service

This solution meets the requirements because it uses the following components and features:

AWS Transfer Family SFTP internal server: This allows the application to securely transfer order files from the on-premises ERP system to AWS using the SFTP protocol over a private connection. The internal server is deployed in two Availability Zones for high availability and fault tolerance.

Amazon S3 storage: This provides scalable, durable, and cost-effective object storage for the order files. Amazon S3 also supports encryption at rest and in transit, as well as lifecycle policies and versioning for data protection and compliance.

AWS Lambda function: This enables the application to process the order files in a serverless manner, without provisioning or managing servers. The Lambda function can perform any custom logic or transformation on the order files, such as validating, parsing, or enriching the data.

Transfer Family managed workflow: This simplifies the orchestration of the file processing tasks by triggering the Lambda function as soon as a file is uploaded to the SFTP server. The managed workflow also provides error handling, retry policies, and logging capabilities.

The AWS Fargate launch type is a serverless way to run containers on Amazon ECS, without having to manage any underlying infrastructure. You only pay for the resources required to run your containers, and AWS handles the provisioning, scaling, and security of the cluster. Amazon EFS is a fully managed, elastic, and scalable file system that can be mounted to multiple containers, and provides high availability and durability. By using AWS Fargate and Amazon EFS, you can run your Docker container image with 50 GB of storage available for temporary files, with the least operational overhead. This solution meets the requirements of the question.

References:

AWS Fargate

Amazon Elastic File System

Using Amazon EFS file systems with Amazon ECS

The company wants to improve the availability and performance of the application, as well as protect it against common web exploits. The company also needs static IP addresses for the application. To meet these requirements, a solutions architect should recommend the following solution:

Put the EC2 instances behind Network Load Balancers (NLBs) in each Region. NLBs are designed to handle millions of requests per second while maintaining high throughput at ultra-low latency. NLBs also support static IP addresses for each Availability Zone, which can be useful for whitelisting or firewalling purposes.

Deploy AWS WAF on the NLBs. AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect availability, security, or performance. AWS WAF lets you define customizable web security rules that control which traffic to allow or block to your web applications.

Create an accelerator using AWS Global Accelerator and register the NLBs as endpoints. AWS Global Accelerator is a service that improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in any AWS Region. It uses the AWS global network to optimize the path from your users to your applications, improving the performance of your TCP and UDP traffic.

This solution will provide high availability across Availability Zones and Regions, improve performance by routing traffic over the AWS global network, protect the application from common web attacks, and provide static IP addresses for the application.

References:

Network Load Balancer

AWS WAF

AWS Global Accelerator

This option is the most cost-effective and scalable way to process the files uploaded to S3. AWS CloudTrail is used to log API calls, not to trigger actions based on them. AWS AppSync is a service for building GraphQL APIs, not for processing files. Amazon Kinesis Data Streams is used to ingest and process streaming data, not to send data to S3. Amazon SNS is a pub/sub service that can be used to notify subscribers of events, not to process files. References:

Using AWS Lambda with Amazon S3

AWS CloudTrail FAQs

What Is AWS AppSync?

[What Is Amazon Kinesis Data Streams?]

[What Is Amazon Simple Notification Service?]

The solution that will reduce the launch time of the application during the next testing phase is to launch the EC2 On-Demand Instances with hibernation turned on and configure EC2 Auto Scaling warm pools. This solution allows the application to resume from a hibernated state instead of starting from scratch, which can save time and resources. Hibernation preserves the memory (RAM) state of the EC2 instances to the root EBS volume and then stops the instances. When the instances are resumed, they restore their memory state from the EBS volume and become productive quickly. EC2 Auto Scaling warm pools can be used to maintain a pool of pre-initialized instances that are ready to scale out when needed. Warm pools can also support hibernated instances, which can further reduce the launch time and cost of scaling out.

The other solutions are not as effective as the first one because they either do not reduce the launch time, do not guarantee availability, or do not use On-Demand Instances as required. Launching two or more EC2 On-Demand Instances with auto scaling features does not reduce the launch time of the application, as each instance still has to go through the initialization process. Launching EC2 Spot Instances does not guarantee availability, as Spot Instances can be interrupted by AWS at any time when there is a higher demand for capacity. Launching EC2 On-Demand Instances with Capacity Reservations does not reduce the launch time of the application, as it only ensures that there is enough capacity available for the instances, but does not pre-initialize them.

References:

Hibernating your instance - Amazon Elastic Compute Cloud

Warm pools for Amazon EC2 Auto Scaling - Amazon EC2 Auto Scaling

This solution meets the requirements because it uses SCPs to restrict the actions that can be performed on cost usage tags in the organization. SCPs are a type of organization policy that you can use to manage permissions in your organization. SCPs specify the maximum permissions for an organization, organizational unit (OU), or account. You can use SCPs to enforce consistent tag policies across your organization and prevent unauthorized or accidental changes to your tags. You can also create exceptions for authorized principals, such as administrators or auditors, who need to modify tags for legitimate purposes.

References:

Service control policies (SCPs) - AWS Organizations

Tag policies - AWS Organizations

https://aws.amazon.com/pt/blogs/aws/amazon-cloudfront-support-for-custom-origins/

You can now create a CloudFront distribution using a custom origin. Each distribution will can point to an S3 or to a custom origin. This could be another storage service, or it could be something more interesting and more dynamic, such as an EC2 instance or even an Elastic Load Balancer

Understanding the Requirement: The company anticipates a spike in traffic during a holiday and wants to ensure the Auto Scaling group can handle the increased load without impacting performance.

Analysis of Options:

CloudWatch Alarm: This reacts to spikes based on metrics like CPU utilization but does not proactively scale before the anticipated demand.

Recurring Scheduled Action: This allows the Auto Scaling group to scale up based on a known schedule, ensuring additional capacity is available before the expected spike.

Increase Min/Max Instances: This could result in unnecessary costs by maintaining higher capacity even when not needed.

SNS Notification: Alerts on scaling events but does not proactively manage scaling to prevent performance issues.

Best Solution for Proactive Scaling:

Create a recurring scheduled action: This approach ensures that the Auto Scaling group scales up before the peak demand, providing the necessary capacity proactively without manual intervention.

References:

Scheduled Scaling for Auto Scaling

These options are the most suitable ways to configure the network architecture to provide the lowest possible latency between nodes. Option A enables and configures enhanced networking on each EC2 instance, which is a feature that improves the network performance of the instance by providing higher bandwidth, lower latency, and lower jitter. Enhanced networking uses single root I/O virtualization (SR-IOV) or Elastic Fabric Adapter (EFA) to provide direct access to the network hardware. You can enable and configure enhanced networking by choosing a supported instance type and a compatible operating system, and installing the required drivers. Option C runs the EC2 instances in a cluster placement group, which is a logical grouping of instances within a single Availability Zone that are placed close together on the same underlying hardware. Cluster placement groups provide the lowest network latency and the highest network throughput among the placement group options. You can run the EC2 instances in a cluster placement group by creating a placement group and launching the instances into it.

Option B is not suitable because grouping the EC2 instances in separate accounts does not provide the lowest possible latency between nodes. Separate accounts are used to isolate and organize resources for different purposes, such as security, billing, or compliance. However, they do not affect the network performance or proximity of the instances. Moreover, grouping the EC2 instances in separate accounts would incur additional costs and complexity, and it would require setting up cross-account networking and permissions.

Option D is not suitable because attaching multiple elastic network interfaces to each EC2 instance does not provide the lowest possible latency between nodes. Elastic network interfaces are virtual network interfaces that can be attached to EC2 instances to provide additional network capabilities, such as multiple IP addresses, multiple subnets, or enhanced security. However, they do not affect the network performance or proximity of the instances. Moreover, attaching multiple elastic network interfaces to each EC2 instance would consume additional resources and limit the instance type choices.

Option E is not suitable because using Amazon EBS optimized instance types does not provide the lowest possible latency between nodes. Amazon EBS optimized instance types are instances that provide dedicated bandwidth for Amazon EBS volumes, which are block storage volumes that can be attached to EC2 instances. EBS optimized instance types improve the performance and consistency of the EBS volumes, but they do not affect the network performance or proximity of the instances. Moreover, using EBS optimized instance types would incur additional costs and may not be necessary for the streaming data workload. References:

Enhanced networking on Linux

Placement groups

Elastic network interfaces

Amazon EBS-optimized instances

To provide the HPC cluster with consistent sub-millisecond latency and high-throughput access to the data on the Snowball Edge Storage Optimized devices, a solutions architect should configure an Amazon FSx for Lustre file system, and integrate it with an Amazon S3 bucket. This solution has the following benefits:

It allows the HPC cluster to access the data on the Snowball Edge devices using a POSIX-compliant file system that is optimized for fast processing of large datasets1.

It enables the data to be imported from the Snowball Edge devices into the S3 bucket using the AWS Snow Family Console or the AWS CLI2. The data can then be accessed from the FSx for Lustre file system using the S3 integration feature3.

It supports high availability and durability of the data, as the FSx for Lustre file system can automatically copy the data to and from the S3 bucket3. The data can also be accessed from other AWS services or applications using the S3 API4.

References:

1: https://aws.amazon.com/fsx/lustre/

2: https://docs.aws.amazon.com/snowball/latest/developer-guide/using-adapter.html

3: https://docs.aws.amazon.com/fsx/latest/LustreGuide/create-fs-linked-data-repo.html

4: https://docs.aws.amazon.com/fsx/latest/LustreGuide/export-data-repo.html

AWS Config is a fully managed service that allows the company to assess, audit, and evaluate the configurations of its AWS resources. It provides a detailed inventory of the resources in use and tracks changes to resource configurations. AWS Config can detect configuration changes and alert the company when changes occur. It also provides a historical view of changes, which is essential for compliance and governance purposes. AWS CloudTrail is a fully managed service that provides a detailed history of API calls made to the company's AWS resources. It records all API activity in the AWS account, including who made the API call, when the call was made, and what resources were affected by the call. This information is critical for security and auditing purposes, as it allows the company to investigate any suspicious activity that might occur on its AWS resources.

Configure provisioned concurrency for the Lambda function that handles the requests. Provisioned concurrency allows you to set the amount of compute resources that are available to the Lambda function, so that it can handle more requests at once and reduce latency. Caching the results of the queries in Amazon S3 could also help to reduce latency, but it would not be as effective as setting up provisioned concurrency. Increasing the size of the database would not help to reduce latency, as this would not increase the number of connections the Lambda function could establish, and establishing a direct connection between the frontend application and the database would bypass the API, which would not be the best solution either.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html

AWS Lambda FAQs

https://aws.amazon.com/lambda/faqs/

https://aws.amazon.com/blogs/networking-and-content-delivery/scaling-network-traffic-inspe ction-using-aws-gateway-load-balancer/

To provide the most high-performing experience for the users of the application, a solutions architect should use a latency routing policy for the Route 53 A record. This policy allows Route 53 to route traffic to the AWS Region that provides the lowest possible latency for the users1. A latency routing policy can also improve the availability of the application, as Route 53 can automatically route traffic to another Region if the primary Region becomes unavailable2.

References:

1: https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/routing-policy.html#routing-policy-latency

2: https://aws.amazon.com/route53/faqs/#Latency_Based_Routing

This solution meets the requirements most securely because it uses IAM roles and the STS AssumeRole API operation to authenticate and authorize the inventory application to access the DynamoDB tables in different accounts. IAM roles are more secure than IAM users or certificates because they do not require long-term credentials or passwords. Instead, IAM roles provide temporary security credentials that are automatically rotated and can be configured with a limited duration. The STS AssumeRole API operation enables you to request temporary credentials for a role that you are allowed to assume. By using this operation, you can delegate access to resources that are in different AWS accounts that you own or that are owned by third parties. The trust policy of the role defines which entities can assume the role, and the permissions policy of the role defines which actions can be performed on the resources. By using this solution, you can avoid hard-coding credentials or certificates in the inventory application, and you can also avoid storing them in Secrets Manager or ACM. You can also leverage the built-in security features of IAM and STS, such as MFA, access logging, and policy conditions.

References:

IAM Roles

STS AssumeRole

Tutorial: Delegate Access Across AWS Accounts Using IAM Roles

These two solutions will optimize the HPC environment for networking and storage. Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance, scalable storage for compute workloads. It is built on the world’s most popular high-performance file system, Lustre, which is designed for applications that require fast storage, such as HPC and machine learning. By configuring the file system with scratch storage, you can achieve sub-millisecond latencies, up to hundreds of GBs/s of throughput, and millions of IOPS. Scratch file systems are ideal for temporary storage and shorter-term processing of data. Data is not replicated and does not persist if a file server fails. For more information, see Amazon FSx for Lustre.

Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. Its custom-built operating system (OS) bypass hardware interface enhances the performance of inter-instance communications, which is critical to scaling HPC and machine learning applications. EFA provides a low-latency, low-jitter channel for inter-instance communications, enabling your tightly-coupled HPC or distributed machine learning applications to scale to thousands of cores. EFA uses libfabric interface and libfabric APIs for communications, which are supported by most HPC programming models. For more information, see Elastic Fabric Adapter.

The other solutions are not suitable for optimizing the HPC environment for networking and storage. AWS Global Accelerator is a networking service that helps you improve the availability, performance, and security of your public applications by using the AWS global network. It provides two global static public IPs, deterministic routing, fast failover, and TCP termination at the edge for your application endpoints. However, it does not support OS-bypass capabilities or high-performance file systems that are required for HPC and machine learning applications. For more information, see AWS Global Accelerator.

Amazon CloudFront is a content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency, high transfer speeds, all within a developer-friendly environment. CloudFront is integrated with AWS services such as Amazon S3, Amazon EC2, AWS Elemental Media Services, AWS Shield, AWS WAF, and AWS Lambda@Edge. However, CloudFront is not designed for HPC and machine learning applications that require high levels of inter-node communications and fast storage. For more information, see [Amazon CloudFront].

AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS. You can simply upload your code and Elastic Beanstalk automatically handles the deployment, from capacity provisioning, load balancing, auto-scaling to application health monitoring. However, Elastic Beanstalk is not optimized for HPC and machine learning applications that require OS-bypass capabilities and high-performance file systems. For more information, see [AWS Elastic Beanstalk].

References: Amazon FSx for Lustre, Elastic Fabric Adapter, AWS Global Accelerator, [Amazon CloudFront], [AWS Elastic Beanstalk].

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

References:

Amazon S3 Storage Lens

S3 Storage Lens Metrics

Amazon Athena provides a cost-effective, serverless way to query data without affecting the performance of DynamoDB. By using the Athena DynamoDB connector, the company can perform the necessary SQL queries without consuming read capacity on the DynamoDB table, which is essential for minimizing impact on provisioned throughput.

Key benefits:

Minimal Impact on Provisioned Capacity: Athena queries do not directly impact DynamoDB’s read capacity, making it ideal for running analytics without affecting the customer-facing workloads.

Cost-Effective: Athena is a serverless solution, meaning you pay only for the queries you run, making it highly cost-effective compared to running a dedicated cluster like Amazon EMR or Redshift.

AWS Documentation: The use of Athena to query DynamoDB through its connector aligns with AWS's best practices for performance efficiency and cost optimization​​.

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory - Information on how to set up and use S3 Inventory.

S3 Batch Operations - Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

Understanding the Requirement: The company needs to automate inventory and updates of diverse OS versions on EC2 instances and summarize common vulnerabilities for monthly reviews.

Analysis of Options:

Systems Manager Patch Manager and Security Hub: Patch Manager automates patching, but Security Hub is more focused on compliance and security posture rather than inventory and vulnerability management.

Systems Manager Patch Manager and Amazon Inspector: Patch Manager automates OS updates, and Amazon Inspector provides vulnerability assessments, making this a comprehensive solution for the requirements.

AWS Shield Advanced and AWS Config: Shield Advanced is for DDoS protection, not suitable for OS patch management and vulnerability reporting.

Amazon GuardDuty and AWS Config: GuardDuty is for threat detection and monitoring, not specifically for patch management and vulnerability assessments.

Best Solution:

Systems Manager Patch Manager and Amazon Inspector: This combination automates OS updates and provides detailed vulnerability assessments, meeting both the inventory and security reporting needs effectively.

References:

AWS Systems Manager Patch Manager

Amazon Inspector

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

References:

Amazon FSx for NetApp ONTAP

NetApp SnapMirror

To make the application highly available across regions:

Deploy the application in a different region using a new ECS cluster and ALB to ensure regional redundancy.

Use Route 53 failover routing to automatically direct traffic to the healthy region in case of failure.

Use DynamoDB Global Tables to ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.

Option F (PrivateLink): PrivateLink is for secure communication, not for cross-region high availability.

AWS References:

DynamoDB Global Tables

Amazon ECS with ALB

When designing a microservice architecture where each microservice interacts with different AWS services, it's essential to follow the principle of least privilege. This means granting each microservice only the permissions it needs to perform its tasks, reducing the risk of unauthorized access or accidental actions.

The recommended approach is to create individual IAM roles with policies that grant each microservice the specific permissions it requires. Then, these roles should be associated with the EC2 instances that run the corresponding microservice. By doing so, each EC2 instance will assume its specific IAM role, and permissions will be automatically managed by AWS.

IAM roles provide temporary credentials via the instance metadata service, eliminating the need to hard-code credentials in your application code, which enhances security.

AWS References:

IAM Roles for Amazon EC2 explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

Best Practices for IAM includes recommendations for implementing the least privilege principle and using IAM roles effectively.

Why the other options are incorrect:

A. Assign an IAM user to each microservice: This requires managing long-term credentials (access keys), which should be avoided. Storing keys in application code is insecure and creates a maintenance burden.

B. Create a single IAM role: This violates the principle of least privilege, as a single role with broad permissions across all services is less secure.

C. Use AWS Organizations: This approach adds unnecessary complexity. Managing permissions at the account level for each microservice is excessive for this use case and doesn't adhere to the principle of least privilege.

Understanding the Requirement: The company needs a container solution that can scale across on-premises, hybrid, and cloud environments, with a load balancer for HTTP traffic.

Analysis of Options:

Fargate Launch Type and ECS Anywhere: Using Fargate for cloud-based containers and ECS Anywhere for on-premises containers provides a unified management experience across environments without needing to manage infrastructure.

Application Load Balancer: Suitable for HTTP traffic and can distribute requests to the ECS services, ensuring scalability and performance.

Network Load Balancer: Typically used for TCP/UDP traffic, not specifically optimized for HTTP traffic.

EC2 Launch Type for ECS and ECS Anywhere with Fargate: Involves managing infrastructure for EC2 instances, increasing operational overhead.

Best Combination of Solutions:

ECS with Fargate Launch Type and ECS Anywhere: This provides flexibility and scalability across hybrid environments with minimal operational overhead.

Application Load Balancer: Optimized for HTTP traffic, ensuring efficient load distribution and scaling for the ECS services.

References:

Amazon ECS on AWS Fargate

Amazon ECS Anywhere

Application Load Balancer

AWS CloudTrail Lake is a fully managed service that allows the collection, storage, and querying of CloudTrail events for both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

https://aws.amazon.com/premiumsupport/knowledge-center/waf-block-common-attacks/#:~:text=To%20protect%20your%20applications%20against,%2C%20query%20string%2C%20or%20URI. ----------------------------------------------------------------------------------------------------------------------- Protect against SQL injection and cross-site scripting To protect your applications against SQL injection and cross-site scripting (XSS) attacks, use the built-in SQL injection and cross-site scripting engines. Remember that attacks can be performed on different parts of the HTTP request, such as the HTTP header, query string, or URI. Configure the AWS WAF rules to inspect different parts of the HTTP request against the built-in mitigation engines.

Amazon RDS Blue/Green Deployments is the ideal solution for upgrading the database version with minimal operational overhead and no data loss. Blue/Green Deployments allows you to create a separate, fully managed "green" environment with the upgraded database version. You can test the new version in the green environment while the "blue" environment continues serving production traffic. Once testing is complete, you can seamlessly switch traffic to the green environment without downtime.

This solution provides:

Fast, non-disruptive upgrade: Traffic is only switched to the new environment after testing, ensuring zero data loss.

Minimal operational overhead: AWS handles the infrastructure management, reducing manual intervention.

Option A (Manual snapshot): This requires manual intervention and involves more operational overhead.

Option B (Native backup/restore): This approach is more labor-intensive and slower than Blue/Green Deployments.

Option C (DMS): AWS DMS adds unnecessary complexity for a simple version upgrade when Blue/Green Deployments can handle the task more efficiently.

AWS References:

Amazon RDS Blue/Green Deployments

Amazon Cognito provides scalable, serverless authentication, and Lambda@Edge is used for authorization, providing low-latency access control at the edge. Amazon CloudFront serves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.

Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.

Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.

Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can handle global delivery efficiently.

AWS References:

Amazon Cognito

Lambda@Edge

Understanding the Requirement: The data science team needs to securely share selective data with the engineering team across multiple AWS accounts with minimal operational overhead.

Analysis of Options:

Copy data to a common account: Involves data duplication and increased storage costs, and requires managing additional permissions.

Lake Formation permissions Grant command: This method can be effective but may involve significant operational overhead if managing permissions across multiple accounts and datasets manually.

AWS Data Exchange: Designed for sharing data externally or between organizations, which adds unnecessary complexity for internal sharing within the same organization.

Lake Formation tag-based access control: Provides a scalable and efficient way to manage access permissions based on tags, allowing fine-grained control and simplified management across accounts.

Best Solution:

Lake Formation tag-based access control: This solution meets the requirements with the least operational overhead, allowing efficient management of cross-account permissions and secure data sharing.

References:

AWS Lake Formation

Tag-based access control

To achieve high availability for the website, two key actions should be taken:

Amazon RDS for MySQL Multi-AZ: By migrating the database to an RDS for MySQL Multi-AZ deployment, the database becomes highly available. Multi-AZ provides automatic failover from the primary database to a standby replica in another Availability Zone, ensuring database availability even in the case of an AZ failure.

Application Load Balancer and Auto Scaling: Deploying an Application Load Balancer (ALB) in front of the EC2 instances ensures that traffic is evenly distributed across the instances. Configuring an Auto Scaling group to run EC2 instances across multiple Availability Zones ensures that the application remains available even if one instance or one AZ becomes unavailable. This setup enhances fault tolerance and improves reliability.

Why Not Other Options?:

Option A (Internet Gateway per AZ): Internet Gateways are region-wide resources and do not need to be provisioned per Availability Zone. This option does not contribute to high availability.

Option C (DynamoDB + Auto Scaling): DynamoDB would require changes to the application code to switch from MySQL, which is not possible per the question's constraints.

Option D (DataSync): AWS DataSync is used for data transfer and synchronization, not for achieving high availability for a database.

AWS References:

Amazon RDS Multi-AZ Deployments - Explanation of how Multi-AZ deployments work in Amazon RDS.

Application Load Balancing - Details on how to configure and use ALB for distributing traffic across multiple instances.

AWS Site-to-Site VPN: This provides a secure and encrypted connection between an on-premises environment and AWS. It is a cost-effective solution suitable for low bandwidth and small traffic needs.

Quick Setup:

Site-to-Site VPN can be quickly set up by configuring a virtual private gateway on the AWS side and a customer gateway on the on-premises side.

It uses standard IPsec protocol to establish the VPN tunnel.

Cost-Effectiveness: Compared to AWS Direct Connect, which requires dedicated physical connections and higher setup costs, a Site-to-Site VPN is less expensive and easier to implement for smaller traffic requirements.

References:

AWS Site-to-Site VPN

This solution offers the most scalable and cost-effective approach with minimal changes to the existing web portal and no code modifications.

Amazon EC2 On-Demand Instances in an Auto Scaling Group: Running the web portal on EC2 On-Demand instances ensures 100% uptime and scalability. The Auto Scaling group will maintain the desired number of instances, automatically scaling up or down as needed, ensuring high availability for the employee web portal.

AWS Lambda for Document Extraction: Lambda is a serverless compute service that allows you to run code in response to events without provisioning or managing servers. By using Lambda to run the document extraction program, you can trigger the function whenever an employee uploads a document. This approach is cost-effective since you only pay for the compute time used by the Lambda function.

No Code Changes Required: This solution integrates with the existing infrastructure with minimal implementation effort and does not require any modifications to the web portal's code.

Why Not Other Options?:

Option B (Spot Instances): Spot Instances are not suitable for workloads requiring 100% uptime, as they can be terminated by AWS with short notice.

Option C (Savings Plan): A Savings Plan could reduce costs but does not address the requirement for running the document extraction program efficiently or without code changes.

Option D (S3 with API Gateway and Lambda): This would require significant changes to the existing web portal setup, including moving the portal to S3 and reconfiguring its architecture, which contradicts the requirement of minimal implementation effort and no code changes.

AWS References:

Amazon EC2 Auto Scaling - Information on how to use Auto Scaling for EC2 instances.

AWS Lambda - Overview of AWS Lambda and its use cases.

This option is the most efficient because it uses API usage plans and API keys, which are features of Amazon API Gateway that allow you to control who can access your API and how much and how fast they can access it1. It also implements API usage plans and API keys to limit the access of users who do not have a subscription, which enables you to create different tiers of access for your API and charge users accordingly. This solution meets the requirement of updating the application so that only users who have a subscription can access premium content. Option A is less efficient because it uses API caching and throttling on the API Gateway API, which are features of Amazon API Gateway that allow you to improve the performance and availability of your API and protect your backend systems from traffic spikes2. However, this does not provide a way to limit the access of users who do not have a subscription. Option B is less efficient because it uses AWS WAF on the API Gateway API, which is a web application firewall service that helps protect your web applications or APIs against common web exploits that may affect availability, compromise security, or consume excessive resources3. However, this does not provide a way to limit the access of users who do not have a subscription. Option C is less efficient because it uses fine-grained IAM permissions to the premium content in the DynamoDB table, which are permissions that allow you to control access to specific items or attributes within a table4. However, this does not provide a way to limit the access of users who do not have a subscription at the API level.

The company is looking for a solution that provides single sign-on (SSO) across multiple AWS accounts while continuing to manage users and groups in their on-premises Active Directory (AD). AWS IAM Identity Center (formerly AWS SSO) is the recommended solution for this type of requirement.

Explanation:

AWS IAM Identity Center provides a centralized identity management solution, enabling single sign-on across multiple AWS accounts and other cloud applications. It can integrate with on-premises Active Directory to leverage existing users and groups.

By configuring a two-way forest trust relationship between AWS Directory Service for Microsoft Active Directory and the company's on-premises Active Directory, users can be authenticated by their on-premises AD and still access AWS resources through IAM Identity Center. This solution allows centralized management of AWS accounts within AWS Organizations.

The two-way trust allows mutual access between the on-premises AD and the AWS Directory Service. This means that users and groups in the on-premises AD can be used for authentication in AWS IAM Identity Center while maintaining the existing identity management system.

AWS References:

AWS IAM Identity Center Documentation

AWS Directory Service for Microsoft Active Directory Trust Relationships

AWS Directory Service Integration with IAM Identity Center

Why the other options are incorrect:

A. Create an Enterprise Edition Active Directory in AWS Directory Service: This would require setting up a new directory and managing it in AWS, which adds unnecessary overhead. The requirement is to continue using the existing on-premises AD, making this option unsuitable.

C. Use AWS Directory Service and create a two-way trust relationship: While this approach establishes a trust between on-premises AD and AWS Directory Service, it does not address the single sign-on (SSO) requirements across multiple AWS accounts through IAM Identity Center.

D. Deploy an identity provider (IdP) on Amazon EC2: This is more complex than necessary and introduces more management overhead. AWS IAM Identity Center natively supports integration with on-premises Active Directory without requiring a custom IdP.

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet. Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

References:

VPC Endpoints

Amazon SQS and VPC Endpoints

DynamoDB Global Tables provide a fully managed, multi-region, and multi-master database solution that allows you to deploy DynamoDB tables in multiple AWS Regions. This ensures high availability and resiliency across different geographical locations, providing a seamless gaming experience for users. Using provisioned capacity mode with auto-scaling ensures cost-efficiency by scaling up or down based on actual demand.

Option A: While on-demand capacity mode is flexible, provisioned capacity with auto-scaling is more cost-effective for predictable workloads.

Option B (DAX): DAX improves read performance, but it doesn't provide the multi-region replication needed for high availability and resiliency.

Option C: DynamoDB Streams with manual cross-region replication adds more complexity and operational overhead compared to Global Tables.

AWS References:

DynamoDB Global Tables

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notifications can be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouples the video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use of Auto Scaling ensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notifications details how to configure notifications for S3 events.

Amazon SQS is a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2 explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

Understanding the Requirement: The order processing system requires multiple independent validation steps, each handled by separate Lambda functions, with each function accessing only the subset of order information it needs. The system should be loosely coupled to accommodate future changes.

Analysis of Options:

Amazon SQS with a new Lambda function for transformation: This involves additional complexity in creating and managing multiple SQS queues and an extra Lambda function for data transformation.

Amazon SNS with message filtering: While SNS supports message filtering, it is more suited for pub/sub messaging patterns rather than event-driven processing requiring fine-grained control over the data sent to each function.

Amazon EventBridge with input transformers: EventBridge is designed for event-driven architectures, allowing for fine-grained control with input transformers that can modify and filter the event data sent to each target Lambda function, ensuring each function receives only the necessary information.

SQS with synchronous Lambda invocations: This approach adds unnecessary complexity with synchronous invocations and is not ideal for an event-driven, loosely coupled architecture.

Best Solution:

Amazon EventBridge with input transformers: This option provides the most flexible, scalable, and loosely coupled architecture, enabling each Lambda function to receive only the required subset of data.

References:

Amazon EventBridge

EventBridge Input Transformer

Understanding the Requirement: The application needs to write to multiple block storage volumes in multiple EC2 Nitro-based instances simultaneously to achieve higher availability.

Analysis of Options:

General Purpose SSD (gp3) with Multi-Attach: Supports Multi-Attach but does not provide the highest performance required for critical applications.

Throughput Optimized HDD (st1) with Multi-Attach: Not suitable for applications requiring high performance and low latency.

Provisioned IOPS SSD (io2) with Multi-Attach: Provides high performance and durability, suitable for applications requiring simultaneous writes and high availability.

General Purpose SSD (gp2) with Multi-Attach: Similar to gp3 but with less flexibility and performance.

Best Solution:

Provisioned IOPS SSD (io2) with Multi-Attach: This solution ensures the highest performance and availability for the application by allowing multiple EC2 instances to attach to and write to the same EBS volume simultaneously.

References:

Amazon EBS Multi-Attach

Provisioned IOPS SSD (io2)

Both Amazon Kinesis and SQS FIFO queues ensure the sequential processing of messages. By using the payment ID as the partition key in Kinesis or as the message group in the SQS FIFO queue, messages are processed in order. Both solutions also allow for long-term retention (up to 10 days) of messages, making them suitable for this payment processing use case.

Option A (DynamoDB): DynamoDB does not guarantee message ordering for real-time processing.

Option C (ElastiCache): ElastiCache is for caching, not suitable for sequential message processing.

Option D (Standard SQS queue): A standard SQS queue does not guarantee ordering of messages.

AWS References:

Amazon Kinesis

Amazon SQS FIFO Queues

Understanding the Requirement: The company needs to monitor and notify changes to the OU hierarchy with minimal operational overhead.

Analysis of Options:

AWS Control Tower with Account Drift Notifications: AWS Control Tower provides automated account provisioning and governance, including drift detection and notifications for changes in the OU hierarchy.

AWS Control Tower with AWS Config: AWS Config provides resource configuration tracking but is more complex compared to drift notifications directly available in Control Tower.

AWS Service Catalog with CloudTrail: While CloudTrail tracks changes, setting up notification mechanisms involves more operational overhead.

AWS CloudFormation with Drift Detection: Suitable for tracking configuration changes but less efficient for monitoring OU hierarchy changes compared to Control Tower's built-in features.

Best Solution:

AWS Control Tower with Account Drift Notifications: Provides a streamlined and efficient way to detect and notify changes in the OU hierarchy with minimal operational overhead.

References:

AWS Control Tower

AWS Control Tower Drift Detection

Requirement Analysis: The company needs to scale throughput for uploading large files to S3 and downloading them to EC2 instances.

S3 Multipart Uploads: This method allows for the parallel upload of parts of a file, improving upload efficiency and reliability.

Parallel Fetching: Fetching multiple byte-ranges in parallel from S3 improves download performance.

Implementation:

For uploads, use the S3 multipart upload API to upload files in parallel.

For downloads, use the S3 API to request multiple byte-ranges concurrently.

Conclusion: These solutions effectively scale throughput and improve the performance of both uploads and downloads.

References

S3 Multipart Upload: Amazon S3 Multipart Upload

Parallel Fetching: S3 Byte-Range Fetches

Understanding the Requirement: The company needs to keep daily EBS snapshots for 1 month and retain monthly snapshots for 7 years due to new regulations.

Analysis of Options:

S3 Glacier Deep Archive: Moving snapshots to S3 Glacier Deep Archive involves additional complexity and might not be the most straightforward approach for EBS snapshots.

EBS Snapshots Archive: This is a cost-effective solution designed specifically for long-term storage of EBS snapshots.

Standard Tier for 7 Years: Keeping snapshots in the standard tier for 7 years is more expensive and does not optimize costs.

EBS Direct APIs to S3: This involves additional operational overhead and is not the most cost-effective approach compared to using EBS Snapshots Archive.

Best Solution:

EBS Snapshots Archive: Adding a policy to move monthly snapshots to the EBS Snapshots Archive for long-term retention is the most cost-effective and administratively simple solution.

References:

Amazon EBS Snapshots

Amazon EBS Snapshots Archive

For processing thousands of images and generating large files while minimizing manual tasks and operational overhead, using AWS Batch is the best solution. AWS Batch allows you to run large-scale, parallel, and managed batch computing jobs without needing to manage the underlying infrastructure.

AWS Batch: Automates the image processing jobs, dynamically allocating the necessary resources based on the job requirements, which reduces operational overhead.

AWS Step Functions: Orchestrates the entire image processing workflow, ensuring that tasks are executed in the correct sequence, improving manageability.

Amazon S3: Stores the processed files, providing scalable and cost-effective storage.

Option A (ECS with EC2 Spot Instances): While cost-effective, managing ECS and Spot Instances involves more operational effort.

Option C (Lambda with EC2 Spot): Lambda functions have size and duration limitations, making them less suited for large image processing tasks.

Option D (EC2 with Step Functions): Managing EC2 instances involves more overhead than using AWS Batch.

AWS References:

AWS Batch

AWS Step Functions

Understanding the Requirement: The company needs programmatic access to its AWS usage costs for the current year and cost forecasts for the next 12 months, with minimal operational overhead.

Analysis of Options:

AWS Cost Explorer API: Provides programmatic access to detailed usage and cost data, including forecast costs. It supports pagination for handling large datasets, making it an efficient solution.

Downloadable AWS Cost Explorer report csv files: While useful, this method requires manual handling of files and does not provide real-time access.

AWS Budgets actions via FTP: This is less suitable as it involves setting up FTP transfers and does not provide the same level of detail and real-time access as the API.

AWS Budgets reports via SMTP: Similar to FTP, this method involves additional setup and lacks the real-time access and detail provided by the API.

Best Option for Minimal Operational Overhead:

AWS Cost Explorer API provides direct, programmatic access to cost data, including detailed usage and forecasting, with minimal setup and operational effort. It is the most efficient solution for integrating cost data into an operational cost dashboard.

References:

AWS Cost Explorer API

AWS Cost and Usage Reports

AWS Transfer Family: This service provides fully managed support for file transfers directly into and out of Amazon S3 using the SFTP, FTPS, and FTP protocols.

SFTP Endpoints:

Set up an AWS Transfer Family server and configure SFTP endpoints to handle the file transfers.

This service is scalable and managed, reducing operational overhead compared to running an SFTP solution on EC2 instances.

Integration with Active Directory:

Choose the AWS Directory Service option as the identity provider for the Transfer Family server.

Use AD Connector to link the on-premises Active Directory with AWS, allowing employees to use their existing AD credentials to access the SFTP service.

Operational Efficiency: This solution leverages managed services for both file transfer and identity management, ensuring minimal changes to the current authentication mechanisms and reducing operational overhead.

References:

AWS Transfer Family

AWS Directory Service and AD Connector

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application’s own pace.

References

Amazon SQS: Amazon SQS Documentation

Creating a VPC endpoint for S3 and configuring a bucket policy to allow access only from the endpoint ensures that only EC2 instances within the VPC can access the S3 bucket. This solution improves security by restricting access at the network level without the need for public internet access.

Option A (IAM policies): IAM policies alone cannot restrict access based on the network location.

Option B and D (Encryption): Encryption secures data at rest but does not restrict network access to the bucket.

AWS References:

Amazon S3 VPC Endpoints

AWS Organizations: AWS Organizations allows you to create multiple AWS accounts and manage them centrally. You can organize accounts into organizational units (OUs) and apply policies to these units.

Organizational Units (OUs):

Create separate OUs for each department: finance, data analytics, and development.

Place the respective AWS accounts for each department into their corresponding OUs.

Service Control Policies (SCPs):

SCPs are policies that can restrict which AWS services and actions are available to accounts in an OU.

Create SCPs to define which services each department can use and attach these policies to the appropriate OUs.

SCPs apply to all IAM users, groups, and roles within the accounts in the OU, providing centralized control over service usage.

Operational Efficiency: Using AWS Organizations and SCPs provides a scalable and centralized way to manage permissions across multiple accounts with minimal operational overhead.

References:

AWS Organizations

Service Control Policies

Understanding the Requirement: The company wants to maximize cost savings for their application over the next three years, with the flexibility to change the instance family and sizes within the next six months based on application popularity and usage.

Analysis of Options:

Compute Savings Plan: This plan offers the most flexibility, allowing the company to change instance families, sizes, and regions. It applies to EC2, AWS Fargate, and AWS Lambda, offering significant cost savings with this flexibility.

EC2 Instance Savings Plan: This plan is less flexible than the Compute Savings Plan, as it only applies to EC2 instances and allows changes within a specific instance family.

Zonal Reserved Instances: These provide a discount on EC2 instances but are tied to a specific availability zone and instance type, offering the least flexibility.

Standard Reserved Instances: These offer discounts on EC2 instances but with more restrictions compared to Savings Plans, particularly when changing instance types and families.

Best Option for Flexibility and Savings:

The Compute Savings Plan is the most cost-effective solution because it allows the company to maintain flexibility while still achieving significant cost savings. This is critical for adapting to changing application demands without being locked into specific instance types or families.

References:

AWS Savings Plans

EC2 Instance Types

Setting the RDS backup retention policy to 30 days for automated backups through AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are not automatically managed by RDS's retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.

Key AWS features:

Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.

Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.

AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups​.

EBS Encryption:

Default EBS Encryption: Can be enabled for new EBS volumes.

Use of AWS KMS: Specify AWS KMS keys to handle encryption and decryption of data transparently.

Amazon RDS Encryption:

RDS Encryption: Encrypts the underlying storage for RDS instances using AWS KMS.

Configuration: Enable encryption when creating the RDS instance or modify an existing instance to enable encryption.

Least Amount of Changes:

Both EBS and RDS support seamless encryption with AWS KMS, requiring minimal changes to the existing infrastructure.

Enables compliance with regulatory requirements without modifying the application.

Operational Efficiency: Using AWS KMS for both EBS and RDS ensures a consistent, managed approach to encryption, simplifying key management and enhancing security.

References:

Amazon EBS Encryption

Amazon RDS Encryption

AWS Key Management Service

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool - Guidance on using SCT for database schema conversions.

AWS Database Migration Service - Detailed documentation on using DMS for data migrations and ongoing replication.

Creating a listener rule on the Application Load Balancer (ALB) to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.

Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.

Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.

AWS References:

ALB Listener Rules

Amazon Neptune: Neptune is a fully managed graph database service that is optimized for storing and querying highly connected data. It supports both property graph and RDF graph models, making it suitable for applications that need to analyze relationships between data entities.

Neptune Streams: Neptune Streams captures changes to the graph and streams these changes to other AWS services. This is useful for applications that need to monitor and respond to changes in real-time, such as providing recommendations based on user interactions and relationships.

Least Operational Overhead: Using Neptune Streams directly with Amazon Neptune ensures that the solution is tightly integrated, reducing the need for additional components and minimizing operational overhead. This integration simplifies the architecture by eliminating the need for a separate service like Kinesis for change processing.

References:

Amazon Neptune Documentation

Neptune Streams Documentation

Key phrase in the Question is must scale read and write capacity. Aurora is only for Read. Amazon DynamoDB has two read/write capacity modes for processing reads and writes on your tables: On-demand Provisioned (default, free-tier eligible) https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user's geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies - Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing - Information on the different types of load balancers in AWS and when to use them.

The most efficient solution for automatically starting and stopping EC2 instances and DB instances on a schedule while minimizing cost and infrastructure maintenance is to create an AWS Lambda function and configure Amazon EventBridge to invoke the function on a schedule.

Option A, scaling EC2 instances by using elastic resize and scaling DB instances to zero outside of business hours, is not feasible as DB instances cannot be scaled to zero.

Option B, exploring AWS Marketplace for partner solutions, may be an option, but it may not be the most efficient solution and could potentially add additional costs.

Option C, launching another EC2 instance and configuring a crontab schedule to run shell scripts that will start and stop the existing EC2 instances and DB instances on a schedule, adds unnecessary infrastructure and maintenance.

Enforce column-level authorization with Amazon QuickSight and AWS Lake Formation https://aws.amazon.com/blogs/big-data/enforce-column-level-authorization-with-amazon-quick sight-and-aws-lake-formation/

Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

References:

Placement Groups

High Performance Computing on AWS

The solution that will meet the requirement of ensuring that all data that is written to the EBS volumes is encrypted at rest is B. Create the EBS volumes as encrypted volumes and attach the encrypted EBS volumes to the EC2 instances. When you create an EBS volume, you can specify whether to encrypt the volume. If you choose to encrypt the volume, all data written to the volume is automatically encrypted at rest using AWS-managed keys. You can also use customer-managed keys (CMKs) stored in AWS KMS to encrypt and protect your EBS volumes. You can create encrypted EBS volumes and attach them to EC2 instances to ensure that all data written to the volumes is encrypted at rest.

https://docs.aws.amazon.com/waf/latest/developerguide/web-acl.html

A rate-based rule in AWS WAF allows the security team to configure thresholds that trigger rate-based rules, which enable AWS WAF to track the rate of requests for a specified time period and then block them automatically when the threshold is exceeded. This provides the ability to prevent HTTP flood attacks with minimal operational overhead.

This option is the most efficient because it uses AWS Key Management Service (AWS KMS), which is a service that makes it easy for you to create and manage cryptographic keys and control their use across a wide range of AWS services and with your applications running on AWS1. It also uses AWS KMS to encrypt the EBS volumes and Aurora database storage at rest, which provides data protection by encrypting your data with encryption keys that you manage23. It also uses AWS Certificate Manager (ACM), which is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and your internal connected resources. It also attaches an ACM certificate to the ALB to encrypt data in transit, which provides data protection by enabling SSL/TLS encryption for connections between clients and the load balancer. This solution meets the requirement of encrypting all data for the application at rest and in transit. Option A is less efficient because it uses AWS KMS certificates on the ALB to encrypt data in transit, which is not possible as AWS KMS does not provide certificates but only keys. It also uses AWS Certificate Manager (ACM) to encrypt the EBS volumes and Aurora database storage at rest, which is not possible as ACM does not provide encryption but only certificates. Option B is less efficient because it uses the AWS root account to log in to the AWS Management Console, which is not recommended as it has unrestricted access to all resources in your account. It also uploads the company’s encryption certificates, which is not necessary as ACM can provide certificates for free. It also selects the option to turn on encryption for all data at rest and in transit for the account, which is not possible as encryption settings are specific to each service and resource. Option D is less efficient because it uses BitLocker to encrypt all data at rest, which is a Windows feature that provides encryption for volumes on Windows servers. However, this does not provide encryption for Aurora database storage at rest, as Aurora runs on Linux servers. It also imports the company’s TLS certificate keys to AWS KMS, which is not necessary as ACM can provide certificates for free. It also attaches the KMS keys to the ALB to encrypt data in transit, which is not possible as ALB requires certificates and not keys.

The most operationally efficient solution to meet these requirements would be to create a backup plan by using AWS Backup to perform nightly backups and copying the backups to another Region. Adding the application's EBS volumes as resources will ensure that the application's EC2 instance configuration and data are backed up, and copying the backups to another Region will ensure that the application is recoverable in a different AWS Region.

Read replica use cases - You have a production database that is taking on normal load & You want to run a reporting application to run some analytics • You create a Read Replica to run the new workload there • The production application is unaffected • Read replicas are used for SELECT (=read) only kind of statements (not INSERT, UPDATE, DELETE)

Amazon DocumentDB (with MongoDB compatibility) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

https://aws.amazon.com/blogs/security/how-to-prevent-uploads-of-unencrypted-objects-to- amazon-s3/#:~:text=Solution%20overview,console%2C%20CLI%2C%20or%20SDK.&text=To%20encrypt%20an%20object%20at,S3%2C%20or%20SSE%2DKMS.

Use AWS Batch on Amazon EC2. AWS Batch is a fully managed batch processing service that can be used to easily run batch jobs on Amazon EC2 instances. It can scale the number of instances to match the workload, allowing the batch job to be completed in the desired time frame with minimal operational overhead.

Using AWS Lambda with Amazon API Gateway - AWS Lambda

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html

AWS Lambda FAQs

https://aws.amazon.com/lambda/faqs/

To meet the requirements of minimizing latency for data transmission from the devices and providing rapid failover to another AWS Region, the best solution would be to use AWS Global Accelerator in combination with a Network Load Balancer (NLB) and Amazon Elastic Container Service (Amazon ECS). AWS Global Accelerator is a service that improves the availability and performance of applications by using static IP addresses (Anycast) to route traffic to optimal AWS endpoints. With Global Accelerator, you can direct traffic to multiple Regions and endpoints, and provide automatic failover to another AWS Region.

AWS Storage Gateway Volume Gateway provides two configurations for connecting to iSCSI storage, namely, stored volumes and cached volumes. The stored volume configuration stores the entire data set on-premises and asynchronously backs up the data to AWS. The cached volume configuration stores recently accessed data on-premises, and the remaining data is stored in Amazon S3. Since the company wants only its recently accessed data to remain stored locally, the cached volume configuration would be the most appropriate. It allows the company to keep frequently accessed data on-premises and reduce the need for scaling its iSCSI storage while still providing access to all data through the AWS cloud. This configuration also provides low-latency access to frequently accessed data and cost-effective off-site backups for less frequently accessed data.

https://docs.amazonaws.cn/en_us/storagegateway/latest/vgw/StorageGatewayConcepts.html#storage-gateway-cached-concepts

This option is the most operationally efficient because it uses AWS Transfer Family to create an FTP server that can store incoming files in Amazon S3 Standard12, which is a low-cost and highly available storage service. It also uses AWS Lambda to process the files and delete them after they are processed, which is a serverless and scalable solution that does not require any batch scheduling or infrastructure management. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which enables near real-time processing of the incoming data files3. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option B is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option C is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses AWS Batch to process the files, which requires managing compute resources and job queues.

Amazon Cognito identity pools assign your authenticated users a set of temporary, limited-privilege credentials to access your AWS resources. The permissions for each user are controlled through IAM roles that you create. https://docs.aws.amazon.com/cognito/latest/developerguide/role-based- access-control.html

https://docs.aws.amazon.com/cli/latest/reference/transfer/describe-server.html

https://aws.amazon.com/ec2/dedicated-hosts/ Amazon EC2 Dedicated Hosts allow you to use your eligible software licenses from vendors such as Microsoft and Oracle on Amazon EC2, so that you get the flexibility and cost effectiveness of using your own licenses, but with the resiliency, simplicity and elasticity of AWS.

• Launching instances within a single AZ and using a cluster placement group provides the lowest network latency and highest bandwidth between instances. This maximizes performance for an in-memory database and high-throughput application.

• Communications between instances in the same AZ and placement group are free, minimizing data transfer charges. Inter-AZ and public IP traffic can incur charges.

• A cluster placement group enables the instances to be placed close together within the AZ, allowing the high network throughput required. Partition groups span AZs, reducing bandwidth.

• Auto Scaling across zones could launch instances in AZs that increase data transfer charges. It may reduce network throughput, impacting performance.

Option A is the most appropriate solution for reducing replication lag without significant changes to the application code and minimizing ongoing operational overhead. Migrating the database to Amazon Aurora MySQL allows for improved replication performance and higher scalability compared to Amazon RDS for MySQL. Aurora Replicas provide faster replication, reducing the replication lag, and Aurora Auto Scaling ensures that there are enough Aurora Replicas to handle the incoming traffic. Additionally, Aurora MySQL native functions can replace the stored procedures, reducing the load on the database and improving performance.

https://docs.aws.amazon.com/autoscaling/application/userguide/what-is-application-auto-scaling.html

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft Active Directory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.

Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.

Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

By configuring the Amazon FSx file share to use an AWS KMS CMK to encrypt the images in the file share, the company can protect the images from unauthorized access and comply with company policy. By using NTFS permission sets on the images, the company can prevent accidental deletion of the images by restricting who can modify or delete them.

References:

Amazon FSx for Windows File Server

Using Microsoft Windows file shares

Option A is the most appropriate solution for reducing replication lag without significant changes to the application code and minimizing ongoing operational overhead. Migrating the database to Amazon Aurora MySQL allows for improved replication performance and higher scalability compared to Amazon RDS for MySQL. Aurora Replicas provide faster replication, reducing the replication lag, and Aurora Auto Scaling ensures that there are enough Aurora Replicas to handle the incoming traffic. Additionally, Aurora MySQL native functions can replace the stored procedures, reducing the load on the database and improving performance.

A because Elasticache, despite being ideal for leaderboards per Amazon, doesn't cache at edge locations. D because FSx has higher performance for low latency needs. https://www.techtarget.com/searchaws/tip/Amazon-FSx-vs-EFS-Compare-the-AWS-file-services "FSx is built for high performance and submillisecond latency using solid-state drive storage volumes. This design enables users to select storage capacity and latency independently. Thus, even a subterabyte file system can have 256 Mbps or higher throughput and support volumes up to 64 TB."

Amazon S3 is an object storage service that can store static files such as images, videos, documents, etc. Amazon EFS is a file storage service that can store files in a hierarchical structure and supports NFS protocol. Amazon FSx for Windows File Server is a file storage service that can store files in a hierarchical structure and supports SMB protocol. Amazon EBS is a block storage service that can store data in fixed-size blocks and attach to EC2 instances.

Based on these definitions, the combination of steps that should be taken to meet the requirements are:

A. Store the static files on Amazon S3. Use Amazon CloudFront to cache objects at the edge. D. Store the server-side code on Amazon FSx for Windows File Server. Mount the FSx for Windows File Server volume on each EC2 instance to share the files.

• Amazon S3 for large-scale, durable, and inexpensive storage of the video content. S3 storage costs are significantly lower than EFS. • Amazon EBS only temporarily during processing. By mounting an EBS volume only when a video needs to be processed, and unmounting it after, the time the content spends on the higher-cost EBS storage is minimized. • The EBS volume can be sized to match the workload needs for active processing, keeping costs lower. The volume does not need to store the entire video library long-term.

Amazon DynamoDB Time to Live (TTL) allows you to define a per-item timestamp to determine when an item is no longer needed. Shortly after the date and time of the specified timestamp, DynamoDB deletes the item from your table without consuming any write throughput. TTL is provided at no extra cost as a means to reduce stored data volumes by retaining only the items that remain current for your workload’s needs.

TTL is useful if you store items that lose relevance after a specific time. The following are example TTL use cases:

Remove user or sensor data after one year of inactivity in an application.

Archive expired items to an Amazon S3 data lake via Amazon DynamoDB Streams and AWS Lambda.

Retain sensitive data for a certain amount of time according to contractual or regulatory obligations.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/ TTL.html

AWS Snowball is a secure data transport solution that accelerates moving large amounts of data into and out of the AWS cloud. It can move up to 80 TB of data at a time, and provides a network bandwidth of up to 50 Mbps, so it is well-suited for the task. Additionally, it is secure and easy to use, making it the ideal solution for this migration.

Create an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system on all web servers. To meet the requirements of providing a shared file store for Linux-based web servers without making changes to the application, using an Amazon EFS file system is the best solution. Amazon EFS is a managed NFS file system service that provides shared access to files across multiple Linux-based instances, which makes it suitable for this use case. Amazon S3 is not ideal for this scenario since it is an object storage service and not a file system, and it requires additional tools or libraries to mount the S3 bucket as a file system. Amazon CloudFront can be used to improve content delivery performance but is not necessary for this requirement. Additionally, Amazon EBS volumes can only be mounted to one instance at a time, so it is not suitable for sharing files across multiple instances.

Session Manager is a fully managed AWS Systems Manager capability. With Session Manager, you can manage your Amazon Elastic Compute Cloud (Amazon EC2) instances, edge devices, on-premises servers, and virtual machines (VMs). You can use either an interactive one-click browser-based shell or the AWS Command Line Interface (AWS CLI). Session Manager provides secure and auditable node management without the need to open inbound ports, maintain bastion hosts, or manage SSH keys. Session Manager also allows you to comply with corporate policies that require controlled access to managed nodes, strict security practices, and fully auditable logs with node access details, while providing end users with simple one-click cross-platform access to your managed nodes. https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager.html

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft Active Directory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.

Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.

Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

References:

Amazon FSx for Windows File Server

Using Microsoft Windows file shares

Since the application has no local data on instances, AMIs alone can meet the RPO by restoring instances from the most recent AMI backup. When combined with automated RDS backups for the database, this provides a complete backup solution for this environment. The other options involving EBS snapshots would be unnecessary given the stateless nature of the instances. AMIs provide all the backup needed for the app tier. This uses native, automated AWS backup features that require minimal ongoing management: - AMI automated backups provide point-in-time recovery for the stateless app tier. - RDS automated backups provide point-in-time recovery for the database.

AURORA is 5x performance improvement over MySQL on RDS and handles more read requests than write,; maintaining high availability = Multi-AZ deployment

By purchasing a Compute Savings Plan, the company can save on the costs of running both EC2 instances and Lambda functions. The Lambda functions can be connected to the private subnet that contains the EC2 instances through a VPC endpoint for AWS services or a VPC peering connection. This provides direct network access to the EC2 instances while keeping the traffic within the private network, which helps to minimize network latency. Optimizing the Lambda functions’ duration, memory usage, number of invocations, and amount of data transferred can help to further minimize costs and improve performance. Additionally, using a private subnet helps to ensure that the EC2 instances are not directly accessible from the public internet, which is a security best practice.

To clone the production data into the test environment with high I/O performance and without affecting the production environment, the best option is to take EBS snapshots of the production EBS volumes and restore them onto new EBS volumes in the test environment. Then, attach the new EBS volumes to EC2 instances in the test environment. This option minimizes the time required to clone the data and ensures that modifications to the cloned data do not affect the production environment. Therefore, option C is the correct answer.

The solution that meets the requirements with the least operational overhead is to create a **Regional AWS WAF web ACL with a rate-based rule** and associate the web ACL with the API Gateway stage. This solution will protect the application from HTTP flood attacks by monitoring incoming requests and blocking requests from IP addresses that exceed the predefined rate. Amazon CloudFront distribution with Lambda@Edge in front of the API Gateway Regional API endpoint is also a good solution but it requires more operational overhead than the previous solution. Using Amazon CloudWatch metrics to monitor the Count metric and alerting the security team when the predefined rate is reached is not a solution that can protect against HTTP flood attacks. Creating an Amazon CloudFront distribution in front of the API Gateway Regional API endpoint with a maximum TTL of 24 hours is not a solution that can protect against HTTP flood attacks.

as the policy prevents anyone from doing any EC2 action on any region except us-east-1 and allows only users with source ip 10.100.100.0/24 to terminate instances. So user with source ip 10.100.100.254 can terminate instances in us-east-1 region.

To address the issue of bandwidth limitations on the company's on-premises application, and to minimize the impact on internal user connectivity, a new AWS Direct Connect connection should be established to direct backup traffic through this new connection. This solution will offer a secure, high-speed connection between the company's data center and AWS, which will allow the company to transfer data quickly without consuming internet bandwidth.

Reserved is cheaper than on demand the company has. And it's meet the availabilty (HA) requirement as to spot instance that can be disrupted at any time. PRICING BELOW. On-Demand: 0% There’s no commitment from you. You pay the most with this option. Reserved : 40%-60%1-year or 3-year commitment from you. You save money from that commitment. Spot 50%-90% Ridiculously inexpensive because there’s no commitment from the AWS side.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/monitor -ami-events.html#:~:text=For%20example%2C%20you%20can%20create%20an%20EventBridge%20rule%20that%20detects%20when%20the%20AMI%20creation%20process%20has%20completed%20and%20then%20invokes%20an%20Amazon%20SNS%20topic%20to%20send%20an%20email%20notification%20to%20you.

Creating an Amazon EventBridge (Amazon CloudWatch Events) rule for the CreateImage API call and configuring the target as an Amazon Simple Notification Service (Amazon SNS) topic to send an alert when a CreateImage API call is detected will meet the requirements with the least operational overhead. Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. By creating an EventBridge rule for the CreateImage API call, the company can set up alerts whenever this operation is called within their account. The alert can be sent to an SNS topic, which can then be configured to send notifications to the company's email or other desired destination.

Create an Amazon S3 bucket to store the raw data Create an Amazon FSx for Lustre file system that uses persistent SSD storage Select the option to import data from and export data to Amazon S3 Mount the file system on the EC2 instances. Amazon FSx for Lustre uses SSD storage for sub-millisecond latencies and up to 6 GBps throughput, and can import data from and export data to Amazon S3. Additionally, the option to select persistent SSD storage will ensure that the data is stored on the disk and not lost if the file system is stopped.

Amazon SES is a cost-effective and scalable email service that enables businesses to send and receive email using their own email addresses and domains. Configuring the web instance to send email through Amazon SES is a simple and effective solution that can reduce the time spent resolving complex email delivery issues and minimize operational overhead.

https://docs.aws.amazon.com/prescriptive-guidance/latest/dynamodb-hierarchical-data-model/introduction.html

Move the data objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days - will meet the requirements of keeping the data immediately accessible with high availability and resiliency, while minimizing storage costs. S3 Standard-IA is designed for infrequently accessed data, and it provides a lower storage cost than S3 Standard, while still offering the same low latency, high throughput, and high durability as S3 Standard.

Amazon S3 File Gateway is a hybrid cloud storage service that enables on-premises applications to seamlessly use Amazon S3 cloud storage. It provides a file interface to Amazon S3 and supports SMB and NFS protocols. It also supports S3 Lifecycle policies that can automatically transition data from S3 Standard to S3 Glacier Deep Archive after a specified period of time. This solution will meet the requirements of increasing the company’s available storage space without losing low-latency access to the most recently accessed files and providing file lifecycle management to avoid future storage issues.

https://aws.amazon.com/premiumsupport/knowledge-center/elb-redirect-http-to-https-using-alb/

How can I redirect HTTP requests to HTTPS using an Application Load Balancer? Last updated: 2020-10-30 I want to redirect HTTP requests to HTTPS using Application Load Balancer listener rules. How can I do this? Resolution Reference: https://aws.amazon.com/premiumsupport/knowledge-center/elb-redirect-http-to-https-using-alb/

Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations around the world, providing low latency and high transfer speeds to users accessing the content. Adding a CloudFront distribution in front of the S3 bucket will cache the static website's content at edge locations around the world, decreasing latency for users accessing the website. This solution is also cost-effective as it only charges for the data transfer and requests made by users accessing the content from the CloudFront edge locations. Additionally, this solution provides scalability and reliability benefits as CloudFront can automatically scale to handle increased demand and provide high availability for the website.

 AWS Snowball Edge is a type of Snowball device with on-board storage and compute power for select AWS capabilities. Snowball Edge can do local processing and edge-computing workloads in addition to transferring data between your local environment and the AWS Cloud1. Users can order an AWS Snowball Edge Storage Optimized device that includes Amazon EC2 compute to move 50 TB of data from on premises to AWS. The Storage Optimized device has 80 TB of usable storage and 40 vCPUs of compute power2. Users can copy the data to the device using the AWS OpsHub graphical user interface or the Snowball client command line tool3. Users can also create and run Amazon EC2 instances on the device using Amazon Machine Images (AMIs) that are compatible with the sbe1 instance type. Users can use the Snowball Edge device to transfer the data and run the transformation job locally without using any network bandwidth.

Users can also create a new EC2 instance on AWS to run the transformation application after the data transfer is complete. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud. Users can launch an EC2 instance in the same AWS Region where they send their Snowball Edge device and choose an AMI that matches their application requirements. Users can use the EC2 instance to continue running the transformation job in the AWS Cloud.

These options are operationally efficient because they use Amazon RDS read replicas to offload the reporting workload from the primary DB instance and avoid affecting any document modifications or the addition of new documents1. They also use Reserved Instances for the primary DB instance to reduce costs and On-Demand or Aurora Replicas for the read replicas to scale as needed. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option D is less efficient because it uses AWS Lambda to process the files, which has a maximum execution time of 15 minutes per invocation, which might not be enough for processing each file that needs 3-8 minutes. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which could cause concurrency issues if there are thousands of small data files arriving periodically. Option E is less efficient because it uses Amazon DynamoDB, which is a NoSQL database service that does not support relational queries, which are needed for generating the reports. It also uses fixed write capacity, which could cause throttling or underutilization depending on the incoming data files.

https://www.quora.com/How-can-I-use-DynamoDB-for-storing-metadata-for-Amazon-S3- objects

This solution meets the requirements of scalability, performance, and availability. AWS Lambda can process the photos in parallel and scale up or down automatically depending on the demand. Amazon S3 can store the photos and metadata reliably and durably, and provide high availability and low latency. DynamoDB can store the metadata efficiently and provide consistent performance. This solution also reduces the cost and complexity of managing EC2 instances and EBS volumes.

Option A is incorrect because storing the photos in DynamoDB is not a good practice, as it can increase the storage cost and limit the throughput. Option B is incorrect because Kinesis Data Firehose is not designed for processing photos, but for streaming data to destinations such as S3 or Redshift. Option D is incorrect because increasing the number of EC2 instances and using Provisioned IOPS SSD volumes does not guarantee scalability, as it depends on the load balancer and the application code. It also increases the cost and complexity of managing the infrastructure.

References:

https://aws.amazon.com/certification/certified-solutions-architect-professional/

https://www.examtopics.com/discussions/amazon/view/7193-exam-aws-certified-solutions-architect-professional-topic-1/

https://aws.amazon.com/architecture/

To meet the requirements of detecting and alerting the administrators when PII is shared and automating remediation with the least development effort, the best approach would be to use Amazon S3 bucket as a secure transfer point and scan the objects in the bucket with Amazon Macie. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data stored in Amazon S3. It can be used to classify sensitive data, monitor access to sensitive data, and automate remediation actions.

In this scenario, after uploading the files to the Amazon S3 bucket, the objects can be scanned for PII by Amazon Macie, and if it detects any PII, it can trigger an Amazon Simple Notification Service (SNS) notification to alert the administrators to remove the objects containing PII. This approach requires the least development effort, as Amazon Macie already has pre-built data classification rules that can detect PII in various formats.

Hence, option B is the correct answer.

References:

Amazon Macie User Guide: https://docs.aws.amazon.com/macie/latest/userguide/what-is-macie.html

AWS Well-Architected Framework - Security Pillar: https://docs.aws.amazon.com/wellarchitected/latest/security-pillar/welcome.html

https://docs.aws.amazon.com/eventbridge/latest/userguide/resource-based-policies-eventbridge.html#l ambda-permissions

https://docs.aws.amazon.com/systems-manager/latest/userguide/about-windows-app-patching.html

To provide the product manager access to the Amazon CloudWatch dashboard while following the principle of least privilege, a solution architect should create an IAM user specifically for the product manager and attach the CloudWatch Read Only Access managed policy to the user. This policy allows the user to view the dashboard without being able to make any changes to it. The solution architect should then share the new login credential with the product manager and provide them with the browser URL of the correct dashboard.

https://aws.amazon.com/getting-started/hands-on/build-serverless-web-app-lambda-apigateway-s3-dynamodb-cognito/module-4/

Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito. This example showed similar setup as question: Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, AWS Amplify, Amazon DynamoDB, and Amazon Cognito

https://aws.amazon.com/sqs/features/

By routing incoming requests to Amazon SQS, the company can decouple the job requests from the processing instances. This allows them to scale the number of instances based on the size of the queue, providing more resources when needed. Additionally, using an Auto Scaling group based on the queue size will automatically scale the number of instances up or down depending on the workload. Updating the software to read from the queue will allow it to process the job requests in a more efficient manner, improving the performance of the system.

https://aws.amazon.com/fsx/lustre/

Amazon FSx has native support for Windows file system features and for the industry-standard Server Message Block (SMB) protocol to access file storage over a network. https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

To design the API Gateway URL with the company's domain name and corresponding certificate, the company needs to do the following: 1. Create a Regional API Gateway endpoint: This will allow the company to create an endpoint that is specific to a region. 2. Associate the API Gateway endpoint with the company's domain name: This will allow the company to use its own domain name for the API Gateway URL. 3. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the same Region: This will allow the company to use HTTPS for secure communication with its APIs. 4. Attach the certificate to the API Gateway endpoint: This will allow the company to use the certificate for securing the API Gateway URL. 5. Configure Route 53 to route traffic to the API Gateway endpoint: This will allow the company to use Route 53 to route traffic to the API Gateway URL using the company's domain name.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/PointInTimeRecovery.html

To meet the requirements of immediately accessible records for 1 year and then archived for an additional 9 years with maximum resiliency, we can use S3 Lifecycle policy to transition records from S3 Standard to S3 Glacier Deep Archive after 1 year. And to ensure that the records cannot be deleted by anyone, including administrative and root users, we can use S3 Object Lock in compliance mode for a period of 10 years. Therefore, the correct answer is option C.

(https://aws.amazon.com/cloudfront

In this scenario, the company wants to avoid regional data transfer charges while downloading and uploading images from Amazon S3. To accomplish this at the lowest cost, the NAT gateway should be launched in each availability zone that the EC2 instances are running in. This allows the EC2 instances to route traffic through the local NAT gateway instead of sending traffic across an availability zone boundary and incurring regional data transfer fees. This method will help reduce the data transfer costs since inter-Availability Zone data transfers in a single region are free of charge.

https://docs.aws.amazon.com/ses/latest/dg/send-email-formatted.html

D. Create an Amazon EventBridge (Amazon CloudWatch Events) scheduled event that invokes an AWS Lambda function to query the application's API for the data. This step can be done using AWS Lambda to extract the shipping statistics and organize the data into an HTML format.

B. Use Amazon Simple Email Service (Amazon SES) to format the data and send the report by email. This step can be done by using Amazon SES to send the report to multiple email addresses at the same time every morning.

Therefore, options D and B are the correct choices for this question. Option A is incorrect because Kinesis Data Firehose is not necessary for this use case. Option C is incorrect because AWS Glue is not required to query the application's API. Option E is incorrect because S3 event notifications cannot be used to send the report by email.

https://aws.amazon.com/kinesis/data- firehose/features/?nc=sn&loc=2#:~:text=into%20Amazon%20S3%2C%20Amazon%20Redshift%2C%20Amazon%20OpenSearch%20Service%2C%20Kinesis,Delivery%20streams

"Create an Amazon SQS queue to hold the jobs that needs to be processed. Create an Amazon EC2 Auto Scaling group for the compute application. Set the scaling policy for the Auto Scaling group to add and remove nodes based on the number of items in the SQS queue"

In this case we need to find a durable and loosely coupled solution for storing jobs. Amazon SQS is ideal for this use case and can be configured to use dynamic scaling based on the number of jobs waiting in the queue.To configure this scaling you can use the backlog per instance metric with the target value being the acceptable backlog per instance to maintain. You can calculate these numbers as follows: Backlog per instance: To calculate your backlog per instance, start with the ApproximateNumberOfMessages queue attribute to determine the length of the SQS queue

The basic difference between Snowball and Snowball Edge is the capacity they provide. Snowball provides a total of 50 TB or 80 TB, out of which 42 TB or 72 TB is available, while Amazon Snowball Edge provides 100 TB, out of which 83 TB is available.

Share the existing KMS key with the MSP external account because it has already been used to encrypt the AMI snapshot. https://docs.aws.amazon.com/kms/latest/developerguide/key-policy-modifying-external-accounts.html

To ensure that orders are processed in the order that they are received, the best solution is to use an Amazon SQS FIFO (First-In-First-Out) queue. This type of queue maintains the exact order in which messages are sent and received. In this case, the application can send information about new orders to an Amazon API Gateway REST API, which can then use an API Gateway integration to send a message to an Amazon SQS FIFO queue for processing. The queue can then be configured to invoke an AWS Lambda function to perform the necessary processing on each order. This ensures that orders are processed in the exact order in which they are received.

https://aws.amazon.com/shield/faqs/

https://aws.amazon.com/solutions/implementations/aws-streaming-data-solution-for-amazon-kinesis/

https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock-overview.html

AWS DataSync is a data transfer service that simplifies, automates, and accelerates moving data between on-premises storage systems and AWS storage services over the internet or AWS Direct Connect1. AWS DataSync can transfer data to Amazon FSx for Windows File Server, which is a fully managed file system that is accessible over the industry-standard Server Message Block (SMB) protocol. Amazon FSx for Windows File Server is built on Windows Server, delivering a wide range of administrative features such as user quotas, end-user file restore, and Microsoft Active Directory (AD) integration2. This solution meets the requirements of the question because:

It can migrate more than 100 TB of data to AWS within a reasonable time frame, as AWS DataSync is optimized for high-speed and efficient data transfer1.

It can preserve the intricate directory structure and the millions of small files stored in deep hierarchies of subfolders, as AWS DataSync can handle complex file structures and metadata, such as file names, permissions, and timestamps1.

It can avoid changing the applications to access the data after migration, as Amazon FSx for Windows File Server supports the same SMB protocol and Windows Server features that the company’s on-premises file storage uses2.

It can reduce the operational overhead, as AWS DataSync and Amazon FSx for Windows File Server are fully managed services that handle the tasks of setting up, configuring, and maintaining the data transfer and the file system12.

The visibility timeout begins when Amazon SQS returns a message. During this time, the consumer processes and deletes the message. However, if the consumer fails before deleting the message and your system doesn't call the DeleteMessage action for that message before the visibility timeout expires, the message becomes visible to other consumers and the message is received again. If a message must be received only once, your consumer should delete it within the duration of the visibility timeout. https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

Keyword: SQS queue writes to an Amazon RDS From this, Option D best suite & other Options ruled out [Option A - You can't intruduce one more Queue in the existing one; Option B - only Permission & Option C - Only Retrieves Messages] FIF O queues are designed to never introduce duplicate messages. However, your message producer might introduce duplicates in certain scenarios: for example, if the producer sends a message, does not receive a response, and then resends the same message. Amazon SQS APIs provide deduplication functionality that prevents your message producer from sending duplicates. Any duplicates introduced by the message producer are removed within a 5-minute deduplication interval. For standard queues, you might occasionally receive a duplicate copy of a message (at-least- once delivery). If you use a standard queue, you must design your applications to be idempotent (that is, they must not be affected adversely when processing the same message more than once).

To provide single sign-on (SSO) across all the company's accounts while continuing to manage users and groups in its on-premises self-managed Microsoft Active Directory, the solution is to enable AWS Single Sign-On (SSO) from the AWS SSO console and create a one-way forest trust or a one-way domain trust to connect the company's self-managed Microsoft Active Directory with AWS SSO by using AWS Directory Service for Microsoft Active Directory. This solution is described in the AWS documentation

This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.

Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.

Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.

Why Not Other Options?:

Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.

Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.

Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort.

AWS References:

Amazon FSx for Windows File Server - Overview and benefits of using FSx for Windows File Server.

Windows Server Failover Clustering on AWS - Guide on setting up a Windows Server cluster on AWS.

This is the purpose of bookmarks: "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data." https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

https://docs.amazonaws.cn/en_us/IAM/latest/UserGuide/reference_policies_examples_s3_rw-bucket.html

The policy is separated into two parts because the ListBucket action requires permissions on the bucket while the other actions require permissions on the objects in the bucket. You must use two different Amazon Resource Names (ARNs) to specify bucket-level and object-level permissions. The first Resource element specifies arn:aws:s3:::AdminTools for the ListBucket action so that applications can list all objects in the AdminTools bucket.

https://aws.amazon.com/kms/faqs/ #:~:text=If%20you%20are%20a%20developer%20who%20needs%20to%20digitally,a%20broad%20set%20of%20industry%20and%20regional%20compliance%20regimes.

Migrate the database to Amazon Aurora MySQL - this will let the DB scale on it's own; it'll scale automatically without needing adjustment. Create AMI of the web app and using a launch template - this will make the creating of any future instances of the app seamless. They can then be added to the auto scaling group which will save them money as it will scale up and down based on demand. Using a spot fleet to launch instances- This solves the "MOST cost-effective" portion of the question as spot instances come at a huge discount at the cost of being terminated at any time Amazon deems fit. I think this is why there's a bit of disagreement on this. While it's the most cost effective, it would be a terrible choice if amazon were to terminate that spot instance during a busy period.

"In some cases, this connection alone is not enough. It is always better to guarantee a fallback connection as the backup of DX. There are several options, but implementing it with an AWS Site-To-Site VPN is a real cost-effective solution that can be exploited to reduce costs or, in the meantime, wait for the setup of a second DX."

https://www.proud2becloud.com/hybrid-cloud-networking-backup-aws-direct-connect-network-connection-with-aws-site-to-site-vpn/

ElastiCache, enhances the performance of web applications by quickly retrieving information from fully-managed in-memory data stores. It utilizes Memcached and Redis, and manages to considerably reduce the time your applications would, otherwise, take to read data from disk-based databases. Amazon CloudFront supports dynamic content from HTTP and WebSocket protocols, which are based on the Transmission Control Protocol (TCP) protocol. Common use cases include dynamic API calls, web pages and web applications, as well as an application's static files such as audio and images. It also supports on-demand media streaming over HTTP. AWS Global Accelerator supports both User Datagram Protocol (UDP) and TCP-based protocols. It is commonly used for non-HTTP use cases, such as gaming, IoT and voice over IP. It is also good for HTTP use cases that need static IP addresses or fast regional failover

Amazon RDS Proxy is a service that provides a fully managed, highly available database proxy for Amazon RDS and Aurora databases. It allows you to pool and share database connections, reduce database load, and improve application scalability and availability.

By using Amazon RDS Proxy in front of your Aurora database, you can achieve the following benefits:

You can reduce the number of connections to your database and avoid errors that indicate that there are too many connections. Amazon RDS Proxy handles the connection management and multiplexing for you, so you can use fewer database connections and resources.

You can reduce the failover time by 20% when a read replica is promoted to primary writer. Amazon RDS Proxy automatically detects failures and routes traffic to the new primary instance without requiring changes to your application code or configuration. According to a benchmark test, using Amazon RDS Proxy reduced the failover time from 66 seconds to 53 seconds, which is a 20% improvement.

You can improve the security and compliance of your database access. Amazon RDS Proxy integrates with AWS Secrets Manager and AWS Identity and Access Management (IAM) to enable secure and granular authentication and authorization for your database connections.

Amazon S3 is an object storage service that offers industry-leading scalability, data availability, security, and performance. You can use Amazon S3 to host static content for your website, such as HTML files, images, videos, etc. Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. AWS Fargate is a serverless compute engine for containers that works with both Amazon ECS and Amazon EKS. Fargate makes it easy for you to focus on building your applications by removing the need to provision and manage servers. You can use Amazon ECS with AWS Fargate for compute power for your containerized application logic tier. Amazon RDS is a managed relational database service that makes it easy to set up, operate, and scale a relational database in the cloud. You can use a managed Amazon RDS cluster for the database tier of your application. This solution will simplify deployment and reduce operational costs for your three-tier application. References: https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html https://docs.aws.amazon.com/AmazonECS/latest/developerguide/Welcome.html https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Welcome.ht ml

The solution that meets the requirements with the least operational overhead is to migrate the web tier to Amazon EC2 instances in public subnets, migrate the application tier to EC2 instances in private subnets, and migrate the database tier to Amazon RDS for MySQL in private subnets. This solution allows the company to migrate its three-tier application to AWS by making minimal changes to the architecture, as it preserves the same web, application, and database tiers and uses the same MySQL database engine. The solution also provides a database solution that can restore data to a specific point in time, as Amazon RDS for MySQL supports automated backups and point-in-time recovery. This solution also reduces the operational overhead by using managed services such as Amazon EC2 and Amazon RDS, which handle tasks such as provisioning, patching, scaling, and monitoring.

The other solutions do not meet the requirements as well as the first one because they either involve more changes to the architecture, do not provide point-in-time recovery, or do not follow best practices for security and availability. Migrating the database tier to Amazon Aurora MySQL would require changing the database engine and potentially modifying the application code to ensure compatibility. Migrating the web tier and the application tier to public subnets would expose them to more security risks and reduce their availability in case of a subnet failure. Migrating the database tier to public subnets would also compromise its security and performance. References:

Migrate Your Application Database to Amazon RDS

Amazon RDS for MySQL

Amazon Aurora MySQL

Amazon VPC

A. Create an Amazon API Gateway REST API. Configure the method to use the Lambda function. Enable IAM authentication on the API. This option is the most operationally efficient as it allows you to use API Gateway to handle the HTTPS endpoint and also allows you to use IAM to authenticate the calls to the microservice. API Gateway also provides many additional features such as caching, throttling, and monitoring, which can be useful for a microservice.

https://docs.aws.amazon.com/kinesisanalytics/l atest/dev/what-is.html

This solution satisfies the requirements in the following ways:

• AWS Backup will automatically take full backups of the DynamoDB table on the schedule defined in the backup plan (the first of each month).

• The lifecycle policy can transition backups to cold storage after 6 months, meeting that requirement.

• Setting a 7-year retention period in the backup plan will ensure each backup is retained for 7 years as required.

• AWS Backup manages the backup jobs and lifecycle policies, requiring no custom scripting or management.

The solution that will improve the performance of the data tier is to deploy an Amazon ElastiCache for Redis cluster in front of the existing DB instance and modify the game to use Redis. This solution will enable the game to store and retrieve the location data of the players in a fast and scalable way, as Redis is an in-memory data store that supports geospatial data types and commands. By using ElastiCache for Redis, the game can reduce the load on the RDS for PostgreSQL DB instance, which is not optimized for high-frequency updates and queries of location data. ElastiCache for Redis also supports replication, sharding, and auto scaling to handle the increasing user base of the game.

The other solutions are not as effective as the first one because they either do not improve the performance, do not support geospatial data, or do not leverage caching. Taking a snapshot of the existing DB instance and restoring it with Multi-AZ enabled will not improve the performance of the data tier, as it only provides high availability and durability, but not scalability or low latency. Migrating from Amazon RDS to Amazon OpenSearch Service with OpenSearch Dashboards will not improve the performance of the data tier, as OpenSearch Service is mainly designed for full-text search and analytics, not for real-time location tracking. OpenSearch Service also does not support geospatial data types and commands natively, unlike Redis. Deploying Amazon DynamoDB Accelerator (DAX) in front of the existing DB instance and modifying the game to use DAX will not improve the performance of the data tier, as DAX is only compatible with DynamoDB, not with RDS for PostgreSQL. DAX also does not support geospatial data types and commands.

References:

Amazon ElastiCache for Redis

Geospatial Data Support - Amazon ElastiCache for Redis

Amazon RDS for PostgreSQL

Amazon OpenSearch Service

Amazon DynamoDB Accelerator (DAX)

The solution that meets the requirements is to develop an on-premises custom identity broker application or process that uses AWS Security Token Service (AWS STS) to get short-lived credentials. This solution allows the company to use its existing LDAP directory service to authenticate its users to the AWS Management Console, without requiring SAML compatibility. The custom identity broker application or process can act as a proxy between the LDAP directory service and AWS STS, and can request temporary security credentials for the users based on their LDAP attributes and roles. The users can then use these credentials to access the AWS Management Console via a sign-in URL generated by the identity broker. This solution also enhances security by using short-lived credentials that expire after a specified duration.

The other solutions do not meet the requirements because they either require SAML compatibility or do not provide access to the AWS Management Console. Enabling AWS IAM Identity Center (AWS Single Sign-On) between AWS and the on-premises LDAP would require the LDAP directory service to support SAML 2.0, which is not the case for this scenario. Creating an IAM policy that uses AWS credentials and integrating the policy into LDAP would not provide access to the AWS Management Console, but only to the AWS APIs. Setting up a process that rotates the IAM credentials whenever LDAP credentials are updated would also not provide access to the AWS Management Console, but only to the AWS CLI. Therefore, these solutions are not suitable for the given requirements.

This answer is correct because it allows the web tier to access the database tier by using security groups as a source, which is a recommended best practice for VPC connectivity. Security groups are stateful and can reference other security groups in the same VPC, which simplifies the configuration and maintenance of the firewall rules. By adding an inbound rule to the database tier’s security group, the web tier’s EC2 instances can connect to the RDS instance on port 3306, regardless of their IP addresses or subnets.

References:

Security groups - Amazon Virtual Private Cloud

Best practices and reference architectures for VPC design

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resour ce-ecs-taskdefinition.html

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverage existing AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

References:

https://aws.amazon.com/s3/

https://aws.amazon.com/athena/

https://aws.amazon.com/lambda/

https://aws.amazon.com/textract/

https://aws.amazon.com/comprehend/medical/

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

Using AWS WAF has several benefits. Additional protection against web attacks using criteria that you specify. You can define criteria using characteristics of web requests such as the following: Presence of SQL code that is likely to be malicious (known as SQL injection). Presence of a script that is likely to be malicious (known as cross-site scripting). AWS Firewall Manager simplifies your administration and maintenance tasks across multiple accounts and resources for a variety of protections. https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

https://docs.aws.amazon.com/AmazonRDS/late st/AuroraUserGuide/Aurora.Replication.html

#Aurora.Replication.Replicas Aurora Replicas have two main purposes. You can issue queries to them to scale the read operations for your application. You typically do so by connecting to the reader endpoint of the cluster. That way, Aurora can spread the load for read-only connections across as many Aurora Replicas as you have in the cluster. Aurora Replicas also help to increase availability. If the writer instance in a cluster becomes unavailable, Aurora automatically promotes one of the reader instances to take its place as the new writer.

https://docs.aws.amazon.com/Amazon RDS/latest/AuroraUserGuide/Aurora.Overview.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/field-level-encryption.html

"With Amazon CloudFront, you can enforce secure end-to-end connections to origin servers by using HTTPS. Field-level encryption adds an additional layer of security that lets you protect specific data throughout system processing so that only certain applications can see it."

AWS Fargate is a serverless experience for user applications, allowing the user to concentrate on building applications instead of configuring and managing servers. Fargate also automates resource management, allowing users to easily scale their applications in response to demand.

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_aws-services-that-work-with-iam.html

This solution meets the requirement of migrating a Windows-based application that requires the use of a shared Windows file system attached to multiple Amazon EC2 Windows instances that are deployed across multiple Availability Zones. Amazon FSx for Windows File Server provides fully managed shared storage built on Windows Server, and delivers a wide range of data access, data management, and administrative capabilities. It supports the Server Message Block (SMB) protocol and can be mounted to EC2 Windows instances across multiple Availability Zones.

Option A is incorrect because AWS Storage Gateway in volume gateway mode provides cloud-backed storage volumes that can be mounted as iSCSI devices from on-premises application servers, but it does not support SMB protocol or EC2 Windows instances. Option C is incorrect because Amazon Elastic File System (Amazon EFS) provides a scalable and elastic NFS file system for Linux-based workloads, but it does not support SMB protocol or EC2 Windows instances. Option D is incorrect because Amazon Elastic Block Store (Amazon EBS) provides persistent block storage volumes for use with EC2 instances, but it does not support SMB protocol or attaching multiple instances to the same volume.

References:

https://aws.amazon.com/fsx/windows/

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/using-file-shares.html