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

This option is the mostefficient because it uses data filters, which are specifications that restrict access to certain data in query results and engines integrated with Lake Formation1. Data filters can be used to implement row-level security and cell-level security, which are techniques to prevent access to portions of the data that contain sensitiveinformation2. Data filters can be applied when granting Lake Formation permissions on a Data Catalog table, and can use PartiQL expressions to filter data based on conditions3. This solution meets the requirement of providing a secure solution to prevent access to portions of the data that contain sensitive information. Option A is less efficient because it uses an IAM role that includes permissions to access Lake Formation tables, which is a way to grant access to data in Lake Formation using IAM policies4. However, this does not provide a way to prevent access to portions of the data that contain sensitive information. Option C is less efficient because it uses an AWS Lambda function that removes sensitive information before Lake Formation ingests the data, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency. Option D is less efficient because it uses an AWS Lambda function that periodically queries and removes sensitive information from Lake Formation tables, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency.

The solution that will meet the requirements is to deploy AWS Storage Gateway using cached volumes and use Storage Gateway to store data in Amazon S3 while retaining copies of frequently accessed data subsets locally. This solution will allow the company to migrate its storage infrastructure to AWS while minimizing bandwidth costs, as it will only transfer data that is not cached locally. The solution will also allow for immediate retrieval of data at no additional cost, as the cached volumes will provide low-latencyaccess to the most recently used data. The data stored in Amazon S3 will be durable, scalable, and secure.

The other solutions are not as effective as the first one because they either do not meet the requirements or introduce additional costs or complexity. Deploying Amazon S3 Glacier Vault and enabling expedited retrieval will not meet the requirements, as it will incur additional costs for both storage and retrieval. Amazon S3 Glacier is a low-cost storage service for data archivingand backup, but it has longer retrieval times than Amazon S3. Expedited retrieval is a feature that allows faster access to data, but it charges a higher fee per GB retrieved. Provisioned retrieval capacity is a feature that reserves dedicated capacity for expedited retrievals, but it also charges a monthly fee per provisioned capacity unit. Deploying AWS Storage Gateway using stored volumes to store data locally and use Storage Gateway to asynchronously back up point-in-time snapshots of the data to Amazon S3 will not meet the requirements, as it will not migrate the storage infrastructure to AWS, but only create backups. Stored volumes are volumes that store the primary data locally and back up snapshots to Amazon S3. This solution will not reduce the storage capacity needed on-premises, nor will it leverage the benefits of cloud storage. Deploying AWS Direct Connect to connect with the on-premises data center and configuring AWS Storage Gateway to store data locally and use Storage Gateway to asynchronously back up point-in-time snapshots of the data to Amazon S3 will not meet the requirements, as it will also not migrate the storage infrastructure to AWS, but only create backups. AWS Direct Connect is a service that establishes a dedicated network connection between the on-premises data center and AWS, which can reduce network costs and increase bandwidth. However, this solution will also not reduce the storage capacity needed on-premises, nor will it leverage the benefits of cloud storage.

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 instanceswithin 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

This solution will meet the requirements of high availability and eventual consistency with the least operational overhead. By modifying the Auto Scaling group to use EC2 instances acrossthree Availability Zones, the web application can handle the increase in traffic and tolerate the failure of one or two Availability Zones. By migrating the embedded NoSQL database to Amazon DynamoDB, the company can benefit from a fully managed, scalable, and reliable NoSQL database service that supports eventual consistency. AWS Database Migration Service (AWS DMS) is a cloud service that makes it easy to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. AWS DMS can migrate the embedded NoSQL database to Amazon DynamoDB with minimal downtime and zero data loss.

A read replica is a copy of the primary database that supports read-only queries. By creating a read replica, you can offload the read workload from the primary database and improve its performance. The script can query the read replica without affecting the critical application that uses the primary database. This solution also has the least operational overhead, as you do not need to modify the script, export the data manually, or manage a cache cluster. References:

Working with PostgreSQL, MySQL, and MariaDB Read Replicas

Amazon RDS Performance Insights

This solution will meet the requirements with the most operational efficiency because:

Amazon Cognito user pools provide a secure and scalable user directory that can store and manage user profiles, and handle user sign-up, sign-in, and access control. User pools can also integrate with social identity providers and enterprise identity providers via SAML or OIDC. User pools can issue JSON Web Tokens (JWTs) that can be used to authenticate users and authorize API requests.

Amazon API Gateway REST APIs enable you to create and deploy APIs that expose your backend services to your clients. REST APIs support multiple authorization mechanisms, including Cognito user pools, IAM, Lambda, and custom authorizers. A Cognito authorizer is a type of Lambda authorizer that uses a Cognito user pool as the identity source. When a client makes a request to a REST API method that is configured with a Cognito authorizer, API Gateway verifies the JWTs that are issued by the user pool and grants access based on the token’s claims and the authorizer’s configuration.

By using Cognito user pools and API Gateway REST APIs with a Cognito authorizer, you can achieve a high level of security, scalability, and performance for your web analytics service. You can also leverage the built-in features of Cognito and API Gateway, such as user management, token validation, caching, throttling, and monitoring, without having to implement them yourself. This reduces the operational overhead and complexity of your solution.

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?]

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.