Amazon Web Services DOP-C02 Exam With Confidence Using Practice Dumps

To validate AWS CDK-defined infrastructure, use CDK Assertions (assertions module) to test synthesized CloudFormation templates (e.g., template.hasResourceProperties). Integrate testing into CodeBuild buildspec before deployment, setting OnFailure: ABORT so the pipeline halts if tests fail. This ensures safe continuous delivery following AWS CDK testing best practices.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

During an Amazon ECS service deployment, if tasks fail the Elastic Load Balancing target group health checks, Amazon ECS considers those tasks unhealthy and stops and replaces them. This behavior can cause the deployment to remain incomplete because healthy replacement tasks never become available.

Also, if an essential container in the task exits, the entire task stops. Since the question states that the new tasks enter a stopped state soon after launch, an essential container failure is a direct and highly likely cause.

Why the other options are incorrect:

B. The IAM role used by the DevOps engineer to update the ECS service does not need RunTask permission for the tasks to remain running after launch. A permission issue here would more likely affect the update action itself rather than cause launched tasks to stop shortly afterward.

C. Although CloudWatch Logs configuration problems can affect logging, the missing log group is not the most likely direct reason for ECS tasks to repeatedly stop in this deployment scenario.

D. The task role provides permissions to the application code running inside the container. It is not the role primarily responsible for launching the task. Task startup is typically related to the task execution role, ECS service behavior, container health, and load balancer checks.

The correct answer is A.

Option A is correct because it meets the requirements with the most operational efficiency. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard. Using CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format is also a convenient and integrated solution that leverages the existing CloudWatch dashboards. Attaching the results to the desired CloudWatch dashboard is straightforward and does not require any additional steps or services.

Option B is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction is a valid way to store the data, but it requires creating and managing an S3 bucket and its permissions. Using Amazon Athena to query the S3 bucket and to visualize the results in a pie chart format is also a valid way to analyze the data, but it incurs charges based on the amount of data scanned by each query. Exporting the results from Athena and attaching them to the desired CloudWatch dashboard is also an extra step that adds more overhead and latency.

Option C is incorrect because it uses AWS X-Ray for an inappropriate purpose. Updating the ecommerce application to use AWS X-Ray for instrumentation is a good practice for monitoring and tracing distributed applications, but it is not designed for aggregating product transaction details. Creating a new X-Ray subsegment and adding an annotation for each processed transaction is possible, but it would clutter the X-Ray service map and make it harder to debug performance issues. Using X-Ray traces to query the data and to visualize the results in a pie chart format is also possible, but it would require custom code and logic that are not supported by X-Ray natively. Attaching the results to the desired CloudWatch dashboard is also not supported by X-Ray directly, and would require additional steps or services.

Option D is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard, as in option A. However, creating an AWS Lambda function to aggregate and write the results to Amazon DynamoDB is redundant, as CloudWatch Logs Insights can already perform aggregation queries on log data. Creating a Lambda subscription filter for the log file is also redundant, as CloudWatch Logs Insights can already access log data directly. Attaching the results to the desired CloudWatch dashboard would also require additional steps or services, as DynamoDB does not support native integration with CloudWatch dashboards.