DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

Use AWS Systems Manager to create a new custom patch baseline that includes the default operating system repository and the custom repository. Run the AWS-RunPatchBaseline document by using the Run command to verify and install patches. Use the BaselineOverride API to configure the new custom patch baseline.

Use AWS Direct Connect to integrate the custom repository with the EC2 instances. Use Amazon EventBridge events to deploy the patches.

Use the yum-config-manager command to add the custom repository to the /etc/yum.repos.d configuration. Run the yum-config-manager-enable command to activate the new repository.

Use AWS Systems Manager to create a patch baseline for the default operating system repository and a second patch baseline for the custom repository. Run the AWS-RunPatchBaseline document by using the Run command to verify and install patches. Use the BaselineOverride API to configure the default patch baseline and the custom patch baseline.

Create an AWS CodeArtifact repository and associate the upstream repositories. Create an AWS CodeBuild project that builds the semantic version of the code artifacts. Configure the project to authenticate and connect to the CodeArtifact repository and publish the artifact to the repository.

Use AWS CodeDeploy to upload the generated semantic version of the artifact to an Amazon Elastic File System (Amazon EFS) file system.

Use an AWS CodeBuild project to build the code and to publish the generated semantic version of the artifact to AWS Artifact. Configure build caching in the CodeBuild project.

Create a new AWS CodeArtifact repository. Create an AWS Lambda function that pulls open source packages from the internet and publishes the packages to the repository. Configure AWS CodeDeploy to build semantic versions of the code and publish the versions to the repository.

Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Schedule an Amazon EventBridge rule to run the Lambda function every hour. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scal

Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Create a CloudWatch subscription filter for the application logs with the Lambda function as the target. Create a target tracking scaling policy for the Auto Scaling group that

Create a target tracking scaling policy for the Auto Scaling group. In the target tracking policy, use the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to calculate how many messages are in the queue for each number of instances by using metric math. Use the calculated attribute to scale in and out.

Create an AWS Lambda function that logs the ApproximateNumberOfMessagesVisible attribute of the SQS queue to a CloudWatch Logs log group. Schedule an Amazon EventBridge rule to run the Lambda function every 5 minutes. Create a metric filter to count the number of log events from a CloudWatch logs group. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.