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

To meet the new guideline for application deployment, the company can use a combination of AWS CodePipeline and AWS CloudTrail. A manual approval action in CodePipeline allows the security team to review and approve changes before they are deployed. This action can be configured to pause the pipeline until approval is granted, ensuring that no changes move to production without the necessary sign-off. Additionally, by creating an AWS CloudTrail trail, all actions taken within CodePipeline, including approvals, are recorded and delivered to an Amazon S3 bucket. This provides an audit trail that can be retained for compliance and review purposes.

AWS CodePipeline’s manual approval action provides a way to ensure that a member of the security team can review and approve changes before they are deployed1.

AWS CloudTrail integration with CodePipeline allows for the recording and retention of all pipeline actions, including approvals, which can be stored in Amazon S3 for record-keeping2.

To ensure that CloudTrail cannot be disabled in any member account, the control must be enforced above the individual accounts, at the organization level , and must not be overridable by local administrators. AWS Service Control Policies (SCPs) provide exactly this capability: they define permission guardrails that apply to all IAM users, roles, and even the root user in accounts within an AWS Organizations OU.

By applying an SCP that explicitly denies cloudtrail:StopLogging and cloudtrail:DeleteTrail , the organization prevents any principal in the affected accounts from disabling logging or deleting the trails, regardless of their IAM permissions. This provides strong, centralized enforcement of audit logging.

Option B uses IAM policies in each account. Local administrators with sufficient privileges could modify or detach those policies, defeating the control. Option C only sends notifications and does not prevent the action. Option D with AWS Config auto-remediation still allows a window where CloudTrail is disabled and also can be disabled or modified itself.

Therefore, the most robust and least bypassable approach is to use an SCP at the OU level to deny CloudTrail stop and delete actions.

The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:

Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.

Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).

Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E). Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.