Question 254 of 1,660
SAP-C02 Design for New Solutions Practice Question
A company is designing a serverless data processing pipeline that uses AWS Lambda to process messages from Amazon SQS. Each message requires 5 minutes of processing time. The company expects a steady state of 1,000 messages per second. What is the MOST scalable and cost-effective design?
⚠ Common exam trap
Test-takers frequently assume Lambda is always the best serverless choice, but for long-running, high-throughput tasks (5 minutes per message at 1,000 msg/s), Lambda's 15-minute timeout, concurrency limits, and per-invocation cost make Fargate a more scalable and cost-effective option.
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
✓
Deploy an Amazon ECS service with Fargate that polls the SQS queue. Use an SQS queue with long polling and auto scaling based on queue depth.
Amazon ECS with Fargate provides a fully managed, serverless compute environment that can handle long-running tasks (5 minutes per message) without the 15-minute Lambda timeout limit. By using long polling and auto scaling based on queue depth, the solution scales cost-effectively to process 1,000 messages per second, as Fargate tasks can run indefinitely and scale horizontally to match 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.
- ✓
Deploy an Amazon ECS service with Fargate that polls the SQS queue. Use an SQS queue with long polling and auto scaling based on queue depth.
Why this is correct
ECS can scale to thousands of tasks, each handling one message for 5 minutes, cost-effective and scalable.
- ✗
Use an SQS queue and a Lambda function that processes messages in batches. Set the Lambda function timeout to 15 minutes and use reserved concurrency.
Why it's wrong here
Even with reserved concurrency, default account limits may not support 300k concurrent executions; also cost is high.
- ✗
Use an Amazon MQ broker with ActiveMQ and configure a Lambda function to consume messages from a queue.
Why it's wrong here
Amazon MQ is not serverless, requires provisioning, and is less scalable than SQS for this volume.
- ✗
Configure an SQS queue with a Lambda trigger. Set the Lambda function timeout to 5 minutes and the batch size to 1.
Why it's wrong here
Lambda concurrency limits will throttle at high volume, causing delays and potential loss of messages.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jul 4, 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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