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Exam AWS Certified DevOps Engineer - Professional DOP-C02 topic 1 question 294 discussion

Exam question from Amazon's AWS Certified DevOps Engineer - Professional DOP-C02
Question #: 294
Topic #: 1
[All AWS Certified DevOps Engineer - Professional DOP-C02 Questions]

A company has an application that runs on Amazon EC2 instances in an Auto Scaling group. The application processes a high volume of messages from an Amazon Simple Queue Service (Amazon SQS) queue.

A DevOps engineer noticed that the application took several hours to process a group of messages from the SQS queue. The average CPU utilization of the Auto Scaling group did not cross the threshold of a target tracking scaling policy when processing the messages. The application that processes the SQS queue publishes logs to ‘Amazon CloudWatch Logs.

The DevOps engineer needs to ensure that the queue is processed quickly.

Which solution meets these requirements with the LEAST operational overhead?

  • A. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupInServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Schedule an Amazon EventBridge rule to run the Lambda function every hour. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
  • B. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupInServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Create a CloudWatch subscription filter for the application logs with the Lambda function as the target. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
  • C. Create a target tracking scaling policy for the Auto Scaling group. In the target tracking policy, use the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupInServiceInstances Auto Scaling group attribute to calculate how many messages are in the queue for each number of instances by using metric math. Use the calculated attribute to scale in and out.
  • D. Create an AWS Lambda function that logs the ApproximateNumberOfMessagesVisible attribute of the SQS queue to a CloudWatch Logs log group. Schedule an Amazon EventBridge rule to run the Lambda function every 5 minutes. Create a metric filer to count the number of log events from a CloudWatch logs group. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
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Suggested Answer: C 🗳️
by Ky_24 at Dec. 15, 2024, 2:34 p.m.
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GripZA
5 days, 20 hours ago
Selected Answer: C
both these metrics available in CloudWatch: GroupInServiceInstances: The number of instances that are running as part of the Auto Scaling group. This metric does not include instances that are pending or terminating. ApproximateNumberOfMessagesVisible: The number of messages to be processed. no need for Lambda
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Ky_24
4 months, 1 week ago
Selected Answer: C
Why Option C is Correct: • Target Tracking Scaling Policy: The question specifies that the scaling policy should help process the queue quickly. Using metric math to combine the ApproximateNumberOfMessagesVisible SQS queue attribute with the GroupInServiceInstances Auto Scaling group attribute provides an efficient way to scale based on the actual number of messages and the available instances. • Direct Integration with Auto Scaling: This solution integrates directly with the Auto Scaling group’s target tracking policy, so the scaling of EC2 instances happens automatically and dynamically based on the metrics from SQS, ensuring faster processing of messages.
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