DVA-C02 Deployment Practice Question
A company is deploying a microservices application on Amazon ECS using the Fargate launch type. The application includes a service that must process messages from an Amazon SQS queue. The developer wants to ensure that the service scales based on the number of messages in the queue. Which scaling solution should the developer implement?
⚠ Common exam trap
It's easy for candidates to confuse service auto scaling with scheduled scaling or assume Lambda is the only serverless option, missing that ECS with Fargate can scale based on SQS backlog using a target tracking policy.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue backlog per task.
Amazon ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue backlog per task (calculated as ApproximateNumberOfMessages divided by the number of running tasks) directly correlates the number of ECS tasks to the queue depth. This ensures the service scales up when messages accumulate and scales down when the backlog clears, using a predefined or custom metric that reflects the workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure DynamoDB auto scaling to adjust read capacity based on queue depth.
Why it's wrong here
DynamoDB auto scaling manages the provisioned read/write capacity units for a DynamoDB table, dynamically adjusting them based on actual usage. However, this mechanism is entirely independent of scaling Amazon ECS tasks. It does not influence the number of compute instances or tasks processing messages from an SQS queue, making it irrelevant for the stated goal of scaling microservices based on queue depth.
- ✓
Use Amazon ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue backlog per task.
Why this is correct
Amazon ECS Service Auto Scaling, when configured with a target tracking policy, directly scales the number of ECS tasks in a service. By targeting a specific metric like the SQS queue backlog per task, the service can dynamically adjust capacity to maintain a consistent processing rate. This ensures that as the queue depth increases or decreases, the number of tasks scales proportionally to efficiently process messages, preventing bottlenecks and optimizing resource utilization.
- ✗
Configure AWS Lambda with reserved concurrency and trigger it from the SQS queue.
Why it's wrong here
Configuring AWS Lambda with reserved concurrency sets a maximum limit on the number of concurrent executions for a specific Lambda function, or guarantees a minimum. While Lambda functions can be triggered by SQS, this setting only controls the Lambda service's behavior and does not influence the scaling of Amazon ECS tasks. It is a mechanism for managing Lambda resources, not for dynamically adjusting ECS service capacity based on queue workload.
- ✗
Use Application Auto Scaling with a scheduled scaling policy to increase the number of tasks during peak hours.
Why it's wrong here
Application Auto Scaling with a scheduled scaling policy allows for capacity adjustments at predefined times, such as increasing tasks during anticipated peak hours. This approach is suitable for predictable workloads but lacks the real-time responsiveness required to react to fluctuating SQS queue depths. It cannot dynamically scale the ECS service based on the actual, immediate backlog of messages, potentially leading to either over-provisioning or processing delays.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DVA-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DVA-C02 exam.