AWS Certified CloudOps Engineer Associate SOA-C03 Practice Question
A company runs an Amazon ECS service on the EC2 launch type across two Availability Zones. Sudden traffic bursts increase the number of messages in an Amazon SQS queue that the tasks process, causing 5xx errors before additional tasks start. The DevOps team wants the service to scale proactively based on the queue length while minimizing code maintenance and operational effort. Which solution should they implement?
Move the workload to AWS Fargate and rely on the Fargate launch type's capacity management to handle bursts automatically.
Increase the CPU reservation for each task so that existing tasks can handle the additional workload during bursts.
Configure an Application Auto Scaling target-tracking policy for the ECS service that uses the SQS ApproximateNumberOfMessagesVisible CloudWatch metric.
Deploy an AWS Lambda function that polls the queue and calls the ECS UpdateService API to adjust the desired count.
Amazon ECS integrates with Application Auto Scaling, which can attach a target-tracking or step scaling policy to an ECS service. The policy can reference any Amazon CloudWatch metric, including the default ApproximateNumberOfMessagesVisible metric from the SQS queue. When the metric breaches the defined threshold, Application Auto Scaling automatically increases or decreases the desired task count with no custom code. Creating and scheduling a Lambda function adds operational overhead, migrating to Fargate does not in itself provide proactive scaling, and changing CPU reservations does not address the need to scale on queue depth.
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AWS Certified CloudOps Engineer Associate SOA-C03
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