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

The Amazon SQS service publishes several standard CloudWatch metrics by default, including ApproximateNumberOfMessagesVisible, which represents the number of messages available for retrieval from the queue (i.e., the number of messages waiting to be processed). This is the primary metric to monitor queue backlog and processing delays.

Using ApproximateNumberOfMessagesVisible to monitor the visible messages in the queue gives a direct and near real-time indication of processing delays or backlog.

CloudWatch metrics are automatically collected and available without the need to create custom metrics or Lambda functions, which increases operational efficiency by reducing complexity and maintenance overhead.

Setting a CloudWatch alarm on this metric with a static threshold and a reasonable evaluation period (such as 1 hour) is sufficient to alert the team when the queue grows beyond an expected size.

The alarm can be directly configured to send notifications to an existing SNS topic, maintaining seamless integration with the team’s alerting mechanisms.

The ApproximateNumberOfMessagesDelayed metric refers to messages that are delayed and not available for processing yet due to delay settings, which is not the direct backlog that causes business process delays.

Options C and D introduce unnecessary complexity by requiring Lambda functions and custom metrics or scheduled invocations, which reduce operational efficiency compared to using built-in CloudWatch metrics and alarms.

Reference from AWS Official Documentation and Study Guide:

Amazon SQS Monitoring with CloudWatch Metrics: " Amazon SQS automatically sends metrics to CloudWatch, including ApproximateNumberOfMessagesVisible, which shows the number of messages available for retrieval. " (Source: Amazon SQS Monitoring - AWS Documentation)

Setting CloudWatch Alarms for SQS Queues: " You can create CloudWatch alarms on SQS metrics such as ApproximateNumberOfMessagesVisible to receive notifications when the queue size exceeds a threshold. " (Source: Amazon CloudWatch Alarms - AWS Documentation)

AWS DevOps Engineer Professional Exam Guide: " Efficient alerting for queue backlogs should leverage native CloudWatch metrics to minimize operational overhead. " (Source: Official AWS Certified DevOps Engineer Professional Study Guide)

The requirements demand very low data loss (RPO < 1 minute) and fast recovery (RTO < 10 minutes) across Regions . Snapshot/backup-based approaches (B, D) generally cannot meet an RPO under 1 minute (and restoring snapshots/backups typically pushes RTO higher than 10 minutes for many real-world DB sizes and restore times).

Option A is the only choice that is explicitly built around near-real-time replication to another Region (cross-Region replica) and an operational pattern to promote the standby and update Route 53 to redirect applications quickly during failover.

Multi-AZ addresses in-Region high availability , while the cross-Region replica + promotion addresses cross-Region DR with low RPO and acceptable RTO when automated.

Why the others don’t meet the stated RPO/RTO:

B : Copying snapshots every 5 minutes immediately violates RPO < 1 minute .

C : DMS replication could potentially be low-latency, but the option only mentions notifications (no automated failover/redirect) and does not provide the clearest/most direct managed HA+DR pattern for the required RTO < 10 minutes .

D : “Cross-Region backups every 30 seconds ” is not a standard practical backup cadence for AWS Backup with RDS, and restore-based DR still tends to miss the RTO/RPO targets compared with replica promotion.

Option A is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring automatic rollbacks on the deployment group is not operationally efficient, as it requires manual intervention to fix the errors and redeploy the application.

Option B is correct because setting the deployment configuration to LambdaCanary10Percent10Minutes means that the new version of the application will be deployed to 10 percent of the Lambda functions first, and then to the remaining 90 percent after 10 minutes. This minimizes the impact of errors on customers, as only 10 percent of them will be affected by a faulty deployment. Configuring automatic rollbacks on the deployment group also meets the requirement of reverting to the most recent stable version of the application when an error is detected. Creating a CloudWatch alarm that detects HTTP Bad Gateway errors on API Gateway is a valid way to monitor the health of the application and trigger a rollback if needed.

Option C is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating an SNS topic to send notifications every time a deployment fails is not sufficient to detect errors in the application, as it does not monitor the API Gateway responses.

Option D is incorrect because configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating a metric filter on a CloudWatch log group for API Gateway to monitor HTTP Bad Gateway errors is a valid way to monitor the health of the application, but invoking a new Lambda function to perform a rollback is unnecessary and complex, as CodeDeploy already provides automatic rollback functionality.