CV0-004 Cloud Architecture and Design Practice Question
A cloud engineer is designing a serverless application that processes messages from an Amazon SQS queue. The application must scale automatically based on the number of messages in the queue and must handle failures gracefully by retrying failed messages. Which AWS service should the engineer use to run the application code?
⚠ Common exam trap
The trap here is assuming that any compute service can easily integrate with SQS; only Lambda has native SQS event source mapping that handles scaling and retries automatically.
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
✓
AWS Lambda with an SQS trigger
AWS Lambda with an SQS trigger is the most suitable because it natively integrates with SQS, automatically scales based on the number of messages, and provides built-in retry and dead-letter queue support. Other options require manual scaling configuration or additional components to achieve the same level of integration and simplicity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Step Functions with a state machine
Why it's wrong here
AWS Step Functions can orchestrate workflows and integrate with SQS, but it is not designed to automatically scale based on queue depth. It is better for coordinating multiple steps and managing state, not for high-throughput, event-driven processing that scales with queue size. It would require additional components to achieve the required scaling.
- ✓
AWS Lambda with an SQS trigger
Why this is correct
AWS Lambda supports SQS as an event source. When configured, Lambda automatically polls the queue and invokes the function with batches of messages. It scales based on the number of messages, and failed invocations can be retried or sent to a dead-letter queue. This meets the requirements for automatic scaling and graceful failure handling.
- ✗
Amazon EC2 Auto Scaling group with a custom worker
Why it's wrong here
An EC2 Auto Scaling group can scale based on queue depth using custom CloudWatch metrics, but it requires managing servers, configuring scaling policies, and implementing retry logic manually. It is not serverless and does not provide built-in integration with SQS for automatic scaling and failure handling as seamlessly as Lambda.
- ✗
AWS Fargate with an ECS service
Why it's wrong here
AWS Fargate runs containers without managing servers, but it does not natively integrate with SQS as an event source. You would need to write code to poll the queue and manage scaling based on queue depth, which adds complexity. It is not as straightforward as using a service that directly supports SQS triggers.
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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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This CV0-004 practice question is part of Courseiva's free CompTIA 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 CV0-004 exam.