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AWS Certified Solutions Architect - Professional Questions and Answers

Question 89

A media storage application uploads user photos to Amazon S3 for processing by AWS Lambda functions. Application state is stored in Amazon DynamoOB tables. Users are reporting that some uploaded photos are not being processed properly. The application developers trace the logs and find that Lambda is experiencing photo processing issues when thousands of users upload photos simultaneously. The issues are the result of Lambda concurrency limits and the performance of DynamoDB when data is saved.

Which combination of actions should a solutions architect take to increase the performance and reliability of the application? (Select TWO.)

Options:

Evaluate and adjust the RCUs for the DynamoDB tables.

Evaluate and adjust the WCUs for the DynamoDB tables.

Add an Amazon ElastiCache layer to increase the performance of Lambda functions.

Add an Amazon Simple Queue Service (Amazon SQS) queue and reprocessing logic between Amazon S3 and the Lambda functions.

Use S3 Transfer Acceleration to provide lower latency to users.

Question 90

A company is running an event ticketing platform on AWS and wants to optimize the platform's cost-effectiveness. The platform is deployed on Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 and is backed by an Amazon RDS for MySQL DB instance. The company is developing new application features to run on Amazon EKS with AWS Fargate.

The platform experiences infrequent high peaks in demand. The surges in demand depend on event dates.

Which solution will provide the MOST cost-effective setup for the platform?

Options:

Purchase Standard Reserved Instances for the EC2 instances that the EKS cluster uses in its baseline load. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet predicted peak load for the year.

Purchase Compute Savings Plans for the predicted medium load of the EKS cluster. Scale the cluster with On-Demand Capacity Reservations based on event dates for peaks. Purchase 1-year No Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale out database read replicas during peaks.

Purchase EC2 Instance Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

Purchase Compute Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

Question 91

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?

Options:

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.

Question 92

A company plans to migrate a legacy on-premises application to AWS. The application is a Java web application that runs on Apache Tomcat with a PostgreSQL database.

The company does not have access to the source code but can deploy the application Java Archive (JAR) files. The application has increased traffic at the end of each month.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Launch Amazon EC2 instances in multiple Availability Zones. Deploy Tomcat and PostgreSQL to all the instances by using Amazon EFS mount points. Use AWS Step Functions to deploy additional EC2 instances to scale for increased traffic.

Provision Amazon EKS in an Auto Scaling group across multiple AWS Regions. Deploy Tomcat and PostgreSQL in the container images. Use a Network Load Balancer to scale for increased traffic.

Refactor the Java application into Python-based containers. Use AWS Lambda functions for the application logic. Store application data in Amazon DynamoDB global tables. Use AWS Storage Gateway and Lambda concurrency to scale for increased traffic.

Use AWS Elastic Beanstalk to deploy the Tomcat servers with auto scaling in multiple Availability Zones. Store application data in an Amazon RDS for PostgreSQL database. Deploy Amazon CloudFront and an Application Load Balancer to scale for increased traffic.

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AWS Certified Solutions Architect - Professional Questions and Answers

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