All SAP-C02 Test Inside Amazon Web Services Questions

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Question 32

A company has deployed an application on AWS Elastic Beanstalk. The application uses Amazon Aurora for the database layer. An Amazon CloudFront distribution serves web requests and includes the Elastic Beanstalk domain name as the origin server. The distribution is configured with an alternate domain name that visitors use when they access the application.

Each week, the company takes the application out of service for routine maintenance. During the time that the application is unavailable, the company wants visitors to receive an informational message instead of a CloudFront error message.

A solutions architect creates an Amazon S3 bucket as the first step in the process.

Which combination of steps should the solutions architect take next to meet the requirements? (Choose three.)

Options:

Upload static informational content to the S3 bucket.

Create a new CloudFront distribution. Set the S3 bucket as the origin.

Set the S3 bucket as a second origin in the original CloudFront distribution. Configure the distribution and the S3 bucket to use an origin access identity (OAI).

During the weekly maintenance, edit the default cache behavior to use the S3 origin. Revert the change when the maintenance is complete.

During the weekly maintenance, create a cache behavior for the S3 origin on the new distribution. Set the path pattern to \ Set the precedence to 0. Delete the cache behavior when the maintenance is complete.

During the weekly maintenance, configure Elastic Beanstalk to serve traffic from the S3 bucket.

Question 33

A company is refactoring its on-premises order-processing platform in the AWS Cloud. The platform includes a web front end that is hosted on a fleet of VMs RabbitMQ to connect the front end to the backend, and a Kubernetes cluster to run a containerized backend system to process the orders. The company does not want to make any major changes to the application

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

Options:

Create an AMI of the web server VM Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer Set up Amazon MQ to replace the on-premises messaging queue Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend

Create a custom AWS Lambda runtime to mimic the web server environment Create an Amazon API Gateway API to replace the front-end web servers Set up Amazon MQ to replace the on-premises messaging queue Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend

Create an AMI of the web server VM Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer Set up Amazon MQ to replace the on-premises messaging queue Install Kubernetes on a fleet of different EC2 instances to host the order-processing backend

Create an AMI of the web server VM Create an Amazon EC2 Auto Scaling group that uses the AMI and an Application Load Balancer Set up an Amazon Simple Queue Service (Amazon SQS) queue to replace the on-premises messaging queue Configure Amazon Elastic Kubernetes Service (Amazon EKS) to host the order-processing backend

Question 34

A company uses an on-premises data analytics platform. The system is highly available in a fully redundant configuration across 12 servers in the company's data center.

The system runs scheduled jobs, both hourly and daily, in addition to one-time requests from users. Scheduled jobs can take between 20 minutes and 2 hours to finish running and have tight SLAs. The scheduled jobs account for 65% of the system usage. User jobs typically finish running in less than 5 minutes and have no SLA. The user jobs account for 35% of system usage. During system failures, scheduled jobs must continue to meet SLAs. However, user jobs can be delayed.

A solutions architect needs to move the system to Amazon EC2 instances and adopt a consumption-based model to reduce costs with no long-term commitments. The solution must maintain high availability and must not affect the SLAs.

Which solution will meet these requirements MOST cost-effectively?

Options:

Split the 12 instances across two Availability Zones in the chosen AWS Region. Run two instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run four instances in each Availability Zone as Spot Instances.

Split the 12 instances across three Availability Zones in the chosen AWS Region. In one of the Availability Zones, run all four instances as On-Demand Instances with Capacity Reservations. Run the remaining instances as Spot Instances.

Split the 12 instances across three Availability Zones in the chosen AWS Region. Run two instances in each Availability Zone as On-Demand Instances with a Savings Plan. Run two instances in each Availability Zone as Spot Instances.

Split the 12 instances across three Availability Zones in the chosen AWS Region. Run three instances in each Availability Zone as On-Demand Instances with Capacity Reservations. Run one instance in each Availability Zone as a Spot Instance.

Question 35

An application is using an Amazon RDS for MySQL Multi-AZ DB instance in the us-east-1 Region. After a failover test, the application lost the connections to the database and could not re-establish the connections. After a restart of the application, the application re-established the connections.

A solutions architect must implement a solution so that the application can re-establish connections to the database without requiring a restart.

Which solution will meet these requirements?

Options:

Create an Amazon Aurora MySQL Serverless v1 DB instance. Migrate the RDS DB instance to the Aurora Serverless v1 DB instance. Update the connection settings in the application to point to the Aurora reader endpoint.

Create an RDS proxy. Configure the existing RDS endpoint as a target. Update the connection settings in the application to point to the RDS proxy endpoint.

Create a two-node Amazon Aurora MySQL DB cluster. Migrate the RDS DB instance to the Aurora DB cluster. Create an RDS proxy. Configure the existing RDS endpoint as a target. Update the connection settings in the application to point to the RDS proxy endpoint.

Create an Amazon S3 bucket. Export the database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Configure Amazon Athena to use the S3 bucket as a data store. Install the latest Open Database Connectivity (ODBC) driver for the application. Update the connection settings in the application to point to the Athena endpoint