- A
Use an AWS Lambda function to poll the SQS queue and invoke a new task for each message.
Why wrong: Lambda has a 15-minute timeout and is not designed for long-running CPU-bound processing.
- B
Configure ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue's ApproximateNumberOfMessagesVisible metric.
This directly scales tasks based on queue depth, providing proactive scaling.
- C
Increase the CPU allocation for the task definition to process messages faster.
Why wrong: This is vertical scaling and may not be sufficient; also, it does not add more tasks.
- D
Use a CloudWatch alarm based on CPU utilization to trigger an Auto Scaling action.
Why wrong: CPU utilization is a lagging indicator and may not react quickly enough to queue depth changes.
Quick Answer
The answer is to configure ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue’s ApproximateNumberOfMessagesVisible metric. This is the most effective solution because it directly ties the number of Fargate tasks to the actual work waiting in the queue, using a proportional control loop that maintains a target metric value—such as 1,000 visible messages per task—rather than reacting to secondary indicators like CPU. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of horizontal scaling patterns for event-driven microservices, where the key trap is choosing a CloudWatch alarm on CPU (which lags behind queue growth) or a Lambda-based approach (which fails for long-running CPU-bound tasks). A critical memory tip is “scale to the signal, not the symptom”—the SQS queue depth is the direct signal of demand, while CPU is a delayed symptom. Remember the mnemonic: **SQS Depth = Task Count**—match the metric to the work.
SAP-C02 Continuous Improvement for Existing Solutions Practice Question
This SAP-C02 practice question tests your understanding of continuous improvement for existing solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company runs a containerized microservices application on Amazon ECS using Fargate. The application includes a service that processes messages from an Amazon SQS queue. The service is CPU-bound and takes longer to process messages as the queue depth increases. The team wants to automatically scale the number of tasks based on the queue depth. Which solution is MOST effective?
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
Configure ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue's ApproximateNumberOfMessagesVisible metric.
Option B is correct because ECS Service Auto Scaling can use a target tracking scaling policy with the SQS ApproximateNumberOfMessagesVisible metric to scale the number of tasks. Option A is wrong because CloudWatch alarms on CPU can cause slow reaction. Option C is wrong because Lambda is not suitable for long-running CPU-bound tasks. Option D is wrong because adjusting CPU allocation does not address scaling; it's a vertical scaling approach.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use an AWS Lambda function to poll the SQS queue and invoke a new task for each message.
Why it's wrong here
Lambda has a 15-minute timeout and is not designed for long-running CPU-bound processing.
- ✓
Configure ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue's ApproximateNumberOfMessagesVisible metric.
Why this is correct
This directly scales tasks based on queue depth, providing proactive scaling.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the CPU allocation for the task definition to process messages faster.
Why it's wrong here
This is vertical scaling and may not be sufficient; also, it does not add more tasks.
- ✗
Use a CloudWatch alarm based on CPU utilization to trigger an Auto Scaling action.
Why it's wrong here
CPU utilization is a lagging indicator and may not react quickly enough to queue depth changes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which SAP-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Continuous Improvement for Existing Solutions — study guide chapter
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FAQ
Questions learners often ask
What does this SAP-C02 question test?
Continuous Improvement for Existing Solutions — This question tests Continuous Improvement for Existing Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure ECS Service Auto Scaling with a target tracking scaling policy based on the SQS queue's ApproximateNumberOfMessagesVisible metric. — Option B is correct because ECS Service Auto Scaling can use a target tracking scaling policy with the SQS ApproximateNumberOfMessagesVisible metric to scale the number of tasks. Option A is wrong because CloudWatch alarms on CPU can cause slow reaction. Option C is wrong because Lambda is not suitable for long-running CPU-bound tasks. Option D is wrong because adjusting CPU allocation does not address scaling; it's a vertical scaling approach.
What should I do if I get this SAP-C02 question wrong?
Identify which SAP-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on SAP-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company runs a containerized microservices application on Amazon ECS with Fargate launch type. The application consists of a frontend service and a backend service. The backend service is CPU-intensive and experiences high load during business hours. The operations team observes that the frontend service sometimes returns 503 errors during peak load. The team has already configured an ECS service auto scaling policy for the backend service based on average CPU utilization with a target value of 70%. The backend service is currently running 4 tasks, and the frontend is running 2 tasks. The errors seem to correlate with the backend scaling up. Which solution should the team implement to improve the application's performance and reduce errors?
easy- ✓ A.Update the backend service auto scaling policy to use a combination of CPU and memory utilization metrics with a step scaling policy, and increase the cooldown period to allow tasks to stabilize before additional scaling actions.
- B.Reduce the target CPU utilization for the backend auto scaling policy from 70% to 50% to trigger scaling earlier and keep CPU lower.
- C.Change the backend service from Fargate to EC2 launch type and use larger instance types to provide more CPU capacity.
- D.Increase the desired count of the frontend service to 4 tasks to handle more requests and reduce the load on the backend service.
Why A: Option A is correct because the 503 errors during backend scale-up are likely caused by the backend tasks not being fully ready to handle traffic immediately after launch. By using a combination of CPU and memory utilization metrics with a step scaling policy, the team can make scaling decisions more responsive to actual load patterns. Increasing the cooldown period prevents additional scaling actions from being triggered prematurely, giving new tasks time to stabilize and reducing the window where the backend is overwhelmed, which in turn reduces frontend timeouts.
Last reviewed: Jun 20, 2026
This SAP-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 SAP-C02 exam.
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