Pass the Amazon Web Services AWS Certified Professional DOP-C02 Questions and answers with CertsForce

Configure the buildspec.yml file to specify multiple artifacts with different file sets. Enable local caching for the build process by using source cache mode. Use environment variables to dynamically name artifacts based on the build ID.

Configure the buildspec.yml file to output all files as a single artifact. Enable local caching for the build process by using custom cache mode. Create an AWS Lambda function that is invoked by CodeBuild completion. Program the Lambda function to split the artifact into multiple files and to upload the files to the S3 bucket with dynamic names based on build ID.

Create separate CodeBuild projects for each artifact type. Enable local caching for the build process by using Docker layer cache mode. Configure each project to output a single artifact to the S3 bucket with a dynamic name based on build ID. Use AWS Step Functions to orchestrate the projects in parallel.

Set up CodeBuild to generate a single ZIP artifact that contains all files. Enable S3 caching for the build process. Use AWS CodePipeline with a custom action to extract the files and reorganize the files into multiple artifacts in the S3 bucket. Configure the custom action to dynamically name the files based on the time of the build.

Enable Amazon CodeGuru Profiler. Decorate the handler function with @with_lambda_profiler(). Manually review the recommendation report. Write the secret to AWS Systems Manager Parameter Store as a secure string. Update the SAM templates and the Python code to pull the secret from Parameter Store.

Associate the CodeCommit repository with Amazon CodeGuru Reviewer. Manually check the code review for any recommendations. Choose the option to protect the secret. Update the SAM templates and the Python code to pull the secret from AWS Secrets Manager.

Enable Amazon CodeGuru Profiler. Decorate the handler function with @with_lambda_profiler(). Manually review the recommendation report. Choose the option to protect the secret. Update the SAM templates and the Python code to pull the secret from AWS Secrets Manager.

Associate the CodeCommit repository with Amazon CodeGuru Reviewer. Manually check the code review for any recommendations. Write the secret to AWS Systems Manager Parameter Store as a string. Update the SAM templates and the Python code to pull the secret from Parameter Store.

Check that an Amazon EventBridge rule has been created for the main branch to trigger the pipeline.

Check that the CodePipeline service role has permission to access the CodeCommit repository.

Check that the developer’s IAM role has permission to push to the CodeCommit repository.

Check to see if the pipeline failed to start because of CodeCommit errors in Amazon CloudWatch Logs.

Deploy the Amazon CloudWatch agent as a container sidecar. Configure the CloudWatch agent to retrieve JVM metrics from port 9404. Create CloudWatch alarms on the JVM thread count metric to scale the application. Add a step scaling policy in Fargate to scale up and scale down based on the CloudWatch alarms.

Deploy the Amazon CloudWatch agent as a container sidecar. Configure a metric filter for the JVM thread count metric on the CloudWatch log group for the CloudWatch agent. Add a target tracking policy in Fargate. Select the metric from the metric filter as a scale target.

Create an Amazon Managed Service for Prometheus workspace. Deploy AWS Distro for OpenTelemetry as a container sidecar to publish the JVM metrics from port 9404 to the Prometheus workspace. Configure rules for the workspace to use the JVM thread count metric to scale the application. Add a step scaling policy in Fargate. Select the Prometheus rules to scale up and scaling down.

Create an Amazon Managed Service for Prometheus workspace. Deploy AWS Distro for OpenTelemetry as a container sidecar to retrieve JVM metrics from port 9404 to publish the JVM metrics from port 9404 to the Prometheus workspace. Add a target tracking policy in Fargate. Select the Prometheus metric as a scale target.

Configure the AWS Config ec2-volume-inuse-check managed rule with a configuration changes trigger type and an Amazon EC2 volume resource target. Create a new Amazon CloudWatch Events rule scheduled to execute an AWS Lambda function in 14 days to delete the specified EBS volume.

Use Amazon EC2 and Amazon Data Lifecycle Manager to configure a volume lifecycle policy. Set the interval period for unattached EBS volumes to 14 days and set the retention rule to delete. Set the policy target volumes as *.

Create an Amazon CloudWatch Events rule to execute an AWS Lambda function daily. The Lambda function should find unattached EBS volumes and tag them with the current date, and delete unattached volumes that have tags with dates that are more than 14 days old.

Use AWS Trusted Advisor to detect EBS volumes that have been detached for more than 14 days. Execute an AWS Lambda function that creates a snapshot and then deletes the EBS volume.

Create an AWS CodePipelme configuration and set up a post-commit hook to trigger the pipeline after tests have passed Use AWS CodeDeploy and create a Canary deployment configuration that specifies the percentage of traffic and interval

Create an AWS CodeBuild configuration that triggers when the test code is pushed Use AWS CloudFormation to trigger an AWS CodePipelme configuration that deploys the new Lambda versions and specifies the traffic shift percentage and interval

Create an AWS CodePipelme configuration and set up the source code step to trigger when code is pushed. Set up the build step to use AWS CodeBuild to run the tests Set up an AWS CodeDeploy configuration to deploy, then select the CodeDeployDefault.LambdaLinearlDPercentEvery3Minut.es Option.

Use the AWS CLI to set up a post-commit hook that uploads the code to an Amazon S3 bucket after tests have passed. Set up an S3 event trigger that runs a Lambda function that deploys the new version. Use an interval in the Lambda function to deploy the code over time at the required percentage

Configure an Amazon EventBridge rule to receive new AMI events from Image Builder. Target an AWS Systems Manager Run Command document that updates the launch templates of the Auto Scaling groups with the newest AMI ID.

Configure an Amazon EventBridge rule to receive new AMI events from Image Builder. Target an AWS Lambda function that updates the launch templates of the Auto Scaling groups with the newest AMI ID.

Configure the launch template to use a value from AWS Systems Manager Parameter Store for the AMI ID. Configure the Image Builder pipeline to update the Parameter Store value with the newest AMI ID.

Configure the Image Builder distribution settings to update the launch templates with the newest AMI ID. Configure the Auto Scaling groups to use the newest version of the launch template.

Apply tags lo all the EC2 instances. AWS loT Greengrass devices, and on-premises servers. Use Systems Manager Session Manager to push patches to all the tagged devices.

Use Systems Manager Run Command to schedule patching for the EC2 instances AWS loT Greengrass devices and on-premises servers.

Use Systems Manager Patch Manager to schedule patching loT the EC2 instances AWS loT Greengrass devices and on-premises servers as a Systems Manager maintenance window task.

Configure Amazon EventBridge to monitor Systems Manager Patch Manager for updates to patch baselines. Associate Systems Manager Run Command with the event lo initiate a patch action for all EC2 instances AWS loT Greengrass devices and on-premises servers.

Create an IAM instance profile for Systems Manager Attach the instance profile to all the EC2 instances in the AWS account. For the AWS loT Greengrass devices and on-premises servers create an IAM service role for Systems Manager.

Generate a managed-instance activation Use the Activation Code and Activation ID to install Systems Manager Agent (SSM Agent) on each server in the on-premises environment Update the AWS loT Greengrass IAM token exchange role Use the role to deploy SSM Agent on all the loT devices.

Use a buildspec file in AWS CodeBuild to restore the DB cluster from a snapshot of the production database run integration tests, and drop the restored database after verification.

Deploy the application to production. Configure an audit log of data control language (DCL) operations to capture database activities to perform if verification fails.

Create a snapshot of the DB duster before deploying the application Use the Update requires Replacement property on the DB instance in AWS CloudFormation to deploy the application and apply the changes.

Ensure that the DB cluster is a Multi-AZ deployment. Deploy the application with the updates. Fail over to the standby instance if verification fails.

Enable CloudTrail for control plane logging; deploy Logstash as a ReplicaSet on nodes; use OpenSearch to store and analyze logs.

Enable control plane logging for EKS and send logs to CloudWatch; use CloudWatch Container Insights for node and container logs; use CloudWatch Logs Insights to query logs.

Enable API server control plane logging and send to S3; deploy Kubernetes Event Exporter on nodes; send logs to S3; use Athena and QuickSight for analysis.

Use AWS Distro for OpenTelemetry; stream logs to Firehose; analyze data in Redshift.