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

 The UserData property of the AWS: AutoScaling: LaunchConfiguration resource can be used to specify a script that runs when the EC2 instances are launched. This script can include the ECS cluster name as an environment variable for the ECS agent running on the EC2 instances. This way, the EC2 instances will register with the correct ECS cluster. Option A is incorrect because the AWS: ECS: Cluster resource does not have a property to reference the EC2 instances. Option C is incorrect because the EC2 instances are launched by the Auto Scaling group, not by the AWS: EC2: Instance resource. Option D is incorrect because using a custom resource and a Lambda function is unnecessary and overly complex for this scenario. References: AWS::AutoScaling::LaunchConfiguration, Amazon ECS Container Agent Configuration

Option A is the most cost-effective solution. By configuring a warm pool of EC2 instances in the Stopped state, the company can reduce the time it takes for new instances to be ready to serve requests. When the Auto Scaling group launches a new instance, it can attach the stopped EC2 instance from the warm pool. The instance can then be started up immediately, rather than having to wait for the data to be downloaded and processed. This reduces the overall startup time for the application.

Comprehensive and Detailed Explanation From Exact Extract:

To add an automated test in AWS CodePipeline that checks the HTTP response code of a deployed application, the most straightforward and operationally efficient method is to create an AWS Lambda function that makes an HTTP request to the application URL, checks the response code, and then reports success or failure back to CodePipeline.

The Lambda function can be invoked as an action in the pipeline stage.

If the response code is not 200 OK, the Lambda function can return a failure, causing the pipeline stage to fail and halt further execution.

Option A’s CloudWatch synthetic canary monitors are useful for ongoing monitoring but are not natively integrated as CodePipeline actions to control pipeline flow.

Option C incorrectly uses CodeDeploy action for testing, which is not designed for this purpose.

Option D introduces unnecessary complexity by deploying API Gateway and Device Farm, which is not required for a simple HTTP status check.Therefore, option B provides a clean, simple, and fully automated way to enforce HTTP response validation within the pipeline.

Reference from AWS Official Documentation:

AWS CodePipeline Custom Actions:"You can create Lambda invoke actions in your pipeline stages to run custom validation or testing functions."(AWS CodePipeline User Guide - Lambda Actions)

AWS Lambda Function for Health Checks:"Lambda functions can be used to perform health checks or API response validations and report results back to invoking services."(AWS Lambda Developer Guide)

Comprehensive and Detailed Explanation From Exact Extract:

Amazon CloudWatch Logs now supportsdata protection policiesthat can mask sensitive data in logs at ingestion time usingcustom data identifiers. By creating a custom data identifier matching the employee ID pattern (Emp-\d{6}), the administrator can mask those patterns in new log entries automatically.

Assigning an IAM policy that denies the logs:Unmask action to engineers prevents them from seeing unmasked sensitive data, enforcing least privilege access.

This approach is operationally efficient because it uses built-in CloudWatch Logs data protection capabilities without the need for complex custom code or manual log filtering.

Option C requires writing and managing Lambda functions for log transformation, increasing operational overhead. Option D is more complex and reactive rather than preventing the sensitive data exposure. Option B incorrectly references NotAction and managed identifiers that may not match the custom pattern.