SAP-C02 Exam Dumps - AWS Certified Solutions Architect - Professional

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Question # 17

A company is using AWS CloudFormation to deploy its infrastructure. The company is concerned that, if a production CloudFormation stack is deleted, important data stored in Amazon RDS databases or Amazon EBS volumes might also be deleted.

How can the company prevent users from accidentally deleting data in this way?

Modify the CloudFormation templates to add a DeletionPolicy attribute to RDS and EBS resources.

Configure a stack policy that disallows the deletion of RDS and EBS resources.

Modify 1AM policies to deny deleting RDS and EBS resources that are tagged with an "awsrcloudformation: stack-name" tag.

Use AWS Config rules to prevent deleting RDS and EBS resources.

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Question # 18

A company wants to migrate its on-premises application to AWS. The database for the application stores structured product data and temporary user session data. The company needs to decouple the product data from the user session data. The company also needs to implement replication in another AWS Region for disaster recovery.

Which solution will meet these requirements with the HIGHEST performance?

Create an Amazon RDS DB instance with separate schemas to host the product data and the user session data. Configure a read replica for the DB instance in another Region.

Create an Amazon RDS DB instance to host the product data. Configure a read replica for the DB instance in another Region. Create a global datastore in Amazon ElastiCache for Memcached to host the user session data.

Create two Amazon DynamoDB global tables. Use one global table to host the product data Use the other global table to host the user session data. Use DynamoDB Accelerator (DAX) for caching.

Create an Amazon RDS DB instance to host the product data. Configure a read replica for the DB instance in another Region. Create an Amazon DynamoDB global table to host the user session data

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Question # 19

A company has application services that have been containerized and deployed on multiple Amazon EC2 instances with public IPs. An Apache Kafka cluster has been deployed to the EC2 instances. A PostgreSQL database has been migrated to Amazon RDS for PostgreSQL. The company expects a significant increase of orders on its platform when a new version of its flagship product is released.

What changes to the current architecture will reduce operational overhead and support the product release?

Create an EC2 Auto Scaling group behind an Application Load Balancer. Create additional read replicas for the DB instance. Create Amazon Kinesis data streams and configure the application services to use the data streams. Store and serve static content directly from Amazon S3.

Create an EC2 Auto Scaling group behind an Application Load Balancer. Deploy the DB instance in Multi-AZ mode and enable storage auto scaling. Create Amazon Kinesis data streams and configure the application services to use the data streams. Store and serve static content directly from Amazon S3.

Deploy the application on a Kubernetes cluster created on the EC2 instances behind an Application Load Balancer. Deploy the DB instance in Multi-AZ mode and enable storage auto scaling. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluster. Store static content in Amazon S3 behind an Amazon CloudFront distribution.

Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate and enable auto scaling behind an Application Load Balancer. Create additional read replicas for the DB instance. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluster. Store static content in Amazon S3 behind an Amazon CloudFront distribution.

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Answer:

Explanation:

The correct answer is D. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS) with AWS Fargate and enable auto scaling behind an Application Load Balancer. Create additional read replicas for the DB instance. Create an Amazon Managed Streaming for Apache Kafka cluster and configure the application services to use the cluster. Store static content in Amazon S3 behind an Amazon CloudFront distribution.

Option D meets the requirements of the scenario because it allows you to reduce operational overhead and support the product release by using the following AWS services and features:

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed service that allows you to run Kubernetes applications on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use Amazon EKS to deploy your containerized application services on a Kubernetes cluster that is compatible with your existing tools and processes.

AWS Fargate is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can use AWS Fargate as the launch type for your Amazon EKS pods, which are the smallest deployable units of computing in Kubernetes. You can also enable auto scaling for your pods, which allows you to automatically adjust the number of pods based on the demand or custom metrics.

An Application Load Balancer (ALB) is a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use an ALB to balance the load across your Amazon EKS pods and provide high availability and fault tolerance for your application.

Amazon RDS for PostgreSQL is a fully managed relational database service that supports the PostgreSQL open source database engine. You can create additional read replicas for your DB instance, which are copies of your primary DB instance that can handle read-only queries and improve performance. You can also use read replicas to scale out beyond the capacity of a single DB instance for read-heavy workloads.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open source platform for building real-time data pipelines and streaming applications. You can use Amazon MSK to create and manage a Kafka cluster that is highly available, secure, and compatible with your existing Kafka applications. You can also configure your application services to use the Amazon MSK cluster as a source or destination of streaming data.

Amazon S3 is an object storage service that offers high durability, availability, and scalability. You can store static content such as images, videos, or documents in Amazon S3 buckets, which are containers for objects. You can also serve static content directly from Amazon S3 using public URLs or presigned URLs.

Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. You can use Amazon CloudFront to create a distribution that caches static content from your Amazon S3 bucket at edge locations closer to your users. This can improve the performance and user experience of your application.

Option A is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating additional read replicas for the DB instance would not provide high availability or fault tolerance in case of a failure of the primary DB instance, unlike deploying the DB instance in Multi-AZ mode. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK.

Option B is incorrect because creating an EC2 Auto Scaling group behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances for your containers. Creating Amazon Kinesis data streams would not be compatible with your existing Apache Kafka applications, unlike using Amazon MSK. Storing and serving static content directly from Amazon S3 would not provide optimal performance and user experience, unlike using Amazon CloudFront.

Option C is incorrect because deploying the application on a Kubernetes cluster created on the EC2 instances behind an ALB would not reduce operational overhead as much as using AWS Fargate with Amazon EKS, as you would still need to manage EC2 instances and Kubernetes control plane for your containers. Using Amazon API Gateway to interact with the application would add an unnecessary layer of complexity and cost to your architecture, as you would need to create and maintain an API gateway that proxies requests to your ALB.

Question # 20

A company wants to migrate its on-premises data center to the AWS Cloud. This includes thousands of virtualized Linux and Microsoft Windows servers, SAN storage, Java and PHP applications with MYSQL, and Oracle databases. There are many dependent services hosted either in the same data center or externally.

The technical documentation is incomplete and outdated. A solutions architect needs to understand the current environment and estimate the cloud resource costs after the migration.

Which tools or services should solutions architect use to plan the cloud migration? (Choose three.)

AWS Application Discovery Service

AWS SMS

AWS x-Ray

AWS Cloud Adoption Readiness Tool (CART)

Amazon Inspector

AWS Migration Hub

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Question # 21

A company has a legacy application that runs on multiple .NET Framework components. The components share the same Microsoft SQL Server database and

communicate with each other asynchronously by using Microsoft Message Queueing (MSMQ).

The company is starting a migration to containerized .NET Core components and wants to refactor the application to run on AWS. The .NET Core components require complex orchestration. The company must have full control over networking and host configuration. The application's database model is strongly relational.

Which solution will meet these requirements?

Host the .NET Core components on AWS App Runner. Host the database on Amazon RDS for SQL Server. Use Amazon EventBridge for asynchronous messaging.

Host the .NET Core components on Amazon Elastic Container Service (Amazon ECS) with the AWS Fargate launch type. Host the database on Amazon DynamoDB. Use Amazon Simple Notification Service (Amazon SNS) for asynchronous messaging.

Host the .NET Core components on AWS Elastic Beanstalk. Host the database on Amazon Aurora PostgreSQL Serverless v2. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) for asynchronous messaging.

Host the .NET Core components on Amazon Elastic Container Service (Amazon ECS) with the Amazon EC2 launch type. Host the database on Amazon Aurora MySQL Serverless v2. Use Amazon Simple Queue Service (Amazon SQS) for asynchronous messaging.

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Question # 22

An events company runs a ticketing platform on AWS. The company's customers configure and schedule their events on the platform The events result in large increases of traffic to the platform The company knows the date and time of each customer's events

The company runs the platform on an Amazon Elastic Container Service (Amazon ECS) cluster The ECS cluster consists of Amazon EC2 On-Demand Instances that are in an Auto Scaling group. The Auto Scaling group uses a predictive scaling policy

The ECS cluster makes frequent requests to an Amazon S3 bucket to download ticket assets The ECS cluster and the S3 bucket are in the same AWS Region and the same AWS account Traffic between the ECS cluster and the S3 bucket flows across a NAT gateway

The company needs to optimize the cost of the platform without decreasing the platform's availability

Which combination of steps will meet these requirements? (Select TWO)

Create a gateway VPC endpoint for the S3 bucket

Add another ECS capacity provider that uses an Auto Scaling group of Spot Instances Configure the new capacity provider strategy to have the same weight as the existing capacity provider strategy

Create On-Demand Capacity Reservations for the applicable instance type for the time period of the scheduled scaling policies

Enable S3 Transfer Acceleration on the S3 bucket

Replace the predictive scaling policy with scheduled scaling policies for the scheduled events

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Question # 23

A company is running a data-intensive application on AWS. The application runs on a cluster of hundreds of Amazon EC2 instances. A shared file system also runs on several EC2 instances that store 200 TB of data. The application reads and modifies the data on the shared file system and generates a report. The job runs once monthly, reads a subset of the files from the shared file system, and takes about 72 hours to complete. The compute instances scale in an Auto Scaling group, but the instances that host the shared file system run continuously. The compute and storage instances are all in the same AWS Region.

A solutions architect needs to reduce costs by replacing the shared file system instances. The file system must provide high performance access to the needed data for the duration of the 72-hour run.

Which solution will provide the LARGEST overall cost reduction while meeting these requirements?

Migrate the data from the existing shared file system to an Amazon S3 bucket that uses the S3 Intelligent-Tiering storage class. Before the job runs each month, use Amazon FSx for Lustre to create a new file system with the data from Amazon S3 by using lazy loading. Use the new file system as the shared storage for the duration of the job. Delete the file system when the job is complete.

Migrate the data from the existing shared file system to a large Amazon Elastic Block Store (Amazon EBS) volume with Multi-Attach enabled. Attach the EBS volume to each of the instances by using a user data script in the Auto Scaling group launch template. Use the EBS volume as the shared storage for the duration of the job. Detach the EBS volume when the job is complete.

Migrate the data from the existing shared file system to an Amazon S3 bucket that uses the S3 Standard storage class. Before the job runs each month, use Amazon FSx for Lustre to create a new file system with the data from Amazon S3 by using batch loading. Use the new file system as the shared storage for the duration of the job. Delete the file system when the job is complete.

Migrate the data from the existing shared file system to an Amazon S3 bucket. Before the job runs each month, use AWS Storage Gateway to create a file gateway with the data from Amazon S3. Use the file gateway as the shared storage for the job. Delete the file gateway when the job is complete.

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Question # 24

A company has mounted sensors to collect information about environmental parameters such as humidity and light throughout all the company's factories. The company needs to stream and analyze the data in the AWS Cloud in real time. If any of the parameters fall out of acceptable ranges, the factory operations team must receive a notification immediately.

Which solution will meet these requirements?

Stream the data to an Amazon Kinesis Data Firehose delivery stream. Use AWS Step Functions to consume and analyze the data in the Kinesis Data Firehose delivery stream. use Amazon Simple Notification Service (Amazon SNS) to notify the operations team.

Stream the data to an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. Set up a trigger in Amazon MSK to invoke an AWS Fargate task to analyze the data. Use Amazon Simple Email Service (Amazon SES) to notify the operations team.

Stream the data to an Amazon Kinesis data stream. Create an AWS Lambda function to consume the Kinesis data stream and to analyze the data. Use Amazon Simple Notification Service (Amazon SNS) to notify the operations team.

Stream the data to an Amazon Kinesis Data Analytics application. I-Jse an automatically scaled and containerized service in Amazon Elastic Container Service (Amazon ECS) to consume and analyze the data. use Amazon Simple Email Service (Amazon SES) to notify the operations team.

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