Amazon Web Services Updated DOP-C02 Exam Questions and Answers by ari

Add a stage to the CodePipeline pipeline between the source and deploy stages. Use AWS CodeBuild to create a runtime environment and build commands in the buildspec file to invoke test scripts. If errors are found, use the aws deploy stop-deployment command to stop the deployment.

Add a stage to the CodePipeline pipeline between the source and deploy stages. Use this stage to invoke an AWS Lambda function that will run the test scripts. If errors are found, use the aws deploy stop-deployment command to stop the deployment.

Add a hooks section to the CodeDeploy AppSpec file. Use the AfterAllowTestTraffic lifecycle event to invoke an AWS Lambda function to run the test scripts. If errors are found, exit the Lambda function with an error to initiate rollback.

Add a hooks section to the CodeDeploy AppSpec file. Use the AfterAllowTraffic lifecycle event to invoke the test scripts. If errors are found, use the aws deploy stop-deployment CLI command to stop the deployment.

Download the Amazon CloudWatch Logs container instance from AWS. Configure this instance as a task. Update the application service definitions to include the logging task.

Install the Amazon CloudWatch Logs agent on the ECS instances. Change the logging driver in the ECS task definition to awslogs.

Use Amazon EventBridge to schedule an AWS Lambda function that will run every 60 seconds and will run the Amazon CloudWatch Logs create-export-task command. Then point the output to the logging S3 bucket.

Activate access logging on the ALB. Then point the ALB directly to the logging S3 bucket.

Activate access logging on the target groups that the ECS services use. Then send the logs directly to the logging S3 bucket.

Create an Amazon Kinesis Data Firehose delivery stream that has a destination of the logging S3 bucket. Then create an Amazon CloudWatch Logs subscription filter for Kinesis Data Firehose.

Use Amazon Redshift for the product catalog and Amazon DynamoDB tables for the customer information and purchases.

Use Amazon DynamoDB global tables for the product catalog and regional tables for the customer information and purchases.

Use Aurora with read replicas for the product catalog and additional local Aurora instances in each region for the customer information and purchases.

Use Aurora for the product catalog and Amazon DynamoDB global tables for the customer information and purchases.

Create an Amazon Aurora cluster in one Availability Zone across multiple Regions as the data store Use Aurora's automatic recovery capabilities in the event of a disaster

Create an Amazon Aurora global database in two Regions as the data store. In the event of a failure promote the secondary Region as the primary for the application.

Create an Amazon Aurora multi-master cluster across multiple Regions as the data store. Use a Network Load Balancer to balance the database traffic in different Regions.

Set up the application in two Regions and use Amazon Route 53 failover-based routing that points to the Application Load Balancers in both Regions. Use hearth checks to determine the availability in a given Region. Use Auto Scaling groups in each Region to adjust capacity based on demand.

Set up the application m two Regions and use a multi-Region Auto Scaling group behind Application Load Balancers to manage the capacity based on demand. In the event of a disaster adjust the Auto Scaling group's desired instance count to increase baseline capacity in the failover Region.