Amazon DOP-C02 Exam Questions

Step 2: Creating CloudWatch Metric Filters CloudWatch metric filters can be used to search for specific patterns in log data. The operations team can create filters for certain log patterns and set up alarms based on these filters.

Action: Instruct the operations team to create CloudWatch metric filters to detect patterns in the WAF log output.

Why: Metric filters allow the team to trigger alarms based on specific patterns without needing to manually search through logs.

This corresponds to Option A: Create an Amazon CloudWatch Logs log group. Configure the appropriate AWS WAF web ACL to send log messages to the log group. Instruct the operations team to create CloudWatch metric filters.

* Modify the Build Stage to Add a Test Action with a RunOrder Value of 2:

The build stage in AWS CodePipeline can have multiple actions. By adding a test action with a runOrder value of 2, the test action will execute after the initial build action completes.

* Use AWS CodeBuild as the Action Provider to Run Unit Tests:

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages.

Using CodeBuild to run unit tests ensures that the tests are executed in a controlled environment and that only the code changes that pass the unit tests proceed to the deploy stage.

Example configuration in CodePipeline:

{

'name': 'BuildStage',

'actions': [

{

'name': 'Build',

'actionTypeId': {

'category': 'Build',

'owner': 'AWS',

'provider': 'CodeBuild',

'version': '1'

},

'runOrder': 1

},

{

'name': 'Test',

'actionTypeId': {

'category': 'Test',

'owner': 'AWS',

'provider': 'CodeBuild',

'version': '1'

},

'runOrder': 2

}

]

}

By integrating the unit tests into the build stage and ensuring they run after the build process, the pipeline guarantees that only code changes passing all unit tests are deployed.

AWS CodePipeline

AWS CodeBuild

Using CodeBuild with CodePipeline

This corresponds to Option A: Create an Amazon EventBridge rule that reacts to RDS storage autoscaling events from RDS events. Create an AWS Lambda function that publishes a CloudWatch custom metric. Configure the EventBridge rule to invoke the Lambda function. Visualize the custom metric by using the CloudWatch dashboard.

Step 2: Use an Application Load Balancer and Auto Scaling Group The Application Load Balancer (ALB) is essential to balance traffic across multiple instances, and Auto Scaling ensures the deployment scales automatically to meet demand.

Action: Associate the Auto Scaling group and Application Load Balancer target group with the deployment group.

Why: This configuration ensures that traffic is evenly distributed and that instances automatically scale based on traffic load.

Step 3: Use Custom Deployment Configuration The company requires that traffic be rerouted to at least half of the instances to succeed. AWS CodeDeploy allows you to configure custom deployment settings with specific thresholds for healthy hosts.

Action: Create a custom deployment configuration where 50% of the instances must be healthy.

Why: This ensures that the deployment continues only if at least 50% of the new instances are healthy.

Step 4: Clean Temporary Files Using Hooks Before routing traffic to the new environment, the temporary files generated during the deployment must be deleted. This can be achieved using the BeforeAllowTraffic hook in the appspec.yml file.

Action: Use the BeforeAllowTraffic lifecycle event hook to clean up temporary files before routing traffic to the new environment.

Why: This ensures that the environment is clean before the new instances start serving traffic.

Step 5: Terminate Original Instances After Deployment After a successful deployment, AWS CodeDeploy can automatically terminate the original instances (blue environment) to save costs.

Action: Instruct AWS CodeDeploy to terminate the original instances after the new instances are healthy.

Why: This helps in cost reduction by removing unused instances after the deployment.

This corresponds to Option C: Use an Application Load Balancer and a blue/green deployment. Associate the Auto Scaling group and the Application Load Balancer target group with the deployment group. Use the Automatically copy Auto Scaling group option, and use CodeDeployDefault.HalfAtATime as the deployment configuration. Instruct AWS CodeDeploy to terminate the original instances in the deployment group, and use the BeforeAllowTraffic hook within appspec.yml to delete the temporary files.

This corresponds to Option B: Create an AWS CodeCommit repository to store the Dockerfile and Kubernetes deployment files. Create a pipeline in AWS CodePipeline. Use an Amazon EventBridge event to invoke the pipeline when a newer version of the Dockerfile is committed. Add a step to the pipeline to initiate the AWS CodeBuild project.

* Step 2: Enabling Enhanced Scanning on Amazon ECR and Monitoring Vulnerabilities To scan for vulnerabilities in Docker images, Amazon ECR provides both basic and enhanced scanning options. Enhanced scanning offers deeper and more frequent scans, and integrates with Amazon EventBridge to send notifications based on findings.

Action: Turn on enhanced scanning for the Amazon ECR repository where the Docker images are stored. Use Amazon EventBridge to monitor image scan events and trigger an Amazon SNS notification if any HIGH or CRITICAL vulnerabilities are found.

Why: Enhanced scanning provides a detailed analysis of operating system and programming language package vulnerabilities, which can trigger notifications in real-time.

This corresponds to Option D: Create an AWS CodeBuild project that builds the Docker images and stores the Docker images in an Amazon Elastic Container Registry (Amazon ECR) repository. Turn on enhanced scanning for the ECR repository. Create an Amazon EventBridge rule that monitors ECR image scan events. Configure the EventBridge rule to send an event to an SNS topic when the finding-severity-counts parameter is more than 0 at a CRITICAL or HIGH level.