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

The correct answer is A.

A comprehensive and detailed explanation is:

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.

This solution meets the requirements in the most operationally efficient manner by automating the application restart process on the instances without restarting them. When the CloudWatch alarm enters the ALARM state, the EventBridge rule is triggered, which in turn invokes the Systems Manager Automation runbook that contains the script to restart the application on the instances.

For the critical data, using a warm pool1 can reduce the scale-out latency by having pre-initialized EC2 instances ready to serve the application traffic. Using On-Demand Instances can ensure that the instances are always available and not interrupted by Spot interruptions2.

For the noncritical data, using a second Auto Scaling group with Spot Instances can reduce the cost and leverage the unused capacity of EC23. Using a launch template with the CloudWatch agent4 can enable the collection of memory utilization metrics, which can be used to scale the group based on the memory demand. Adding the second group as a target group for the ALB and modifying the application to use two target groups can enable routing the traffic based on the data type.

 1: Warm pools for Amazon EC2 Auto Scaling 2: Amazon EC2 On-Demand Capacity Reservations 3: Amazon EC2 Spot Instances 4: Metrics collected by the CloudWatch agent