- A
Set the SQS queue’s retention period to 10 years and rely on application retries to eventually succeed.
Why wrong: Retention affects how long messages remain available, but it doesn’t isolate repeatedly failing messages.
- B
Increase visibility timeout to a very large value and avoid dead-letter queues to keep ordering stable.
Why wrong: Long visibility time can delay failures, but it does not provide dead-letter isolation for poison messages.
- C
Configure a redrive policy with a dead-letter queue (DLQ) and set an appropriate visibility timeout greater than the maximum processing time.
A DLQ isolates poison messages after a receive count threshold, and correct visibility timeout prevents premature retries.
- D
Switch the queue to FIFO and remove retries in the Lambda event source mapping entirely.
Why wrong: FIFO and disabling retries do not reliably solve poison message isolation without DLQ-based redriving.
SAA-C03 Design Resilient Architectures Practice Question
This SAA-C03 practice question tests your understanding of design resilient architectures. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: a redrive policy links a source queue to a dead-letter queue (DLQ).. 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.
An order processing workflow uses Amazon SQS as the decoupling layer between a producer and a consumer Lambda function. The consumer intermittently fails due to a downstream dependency. The team has observed that certain “poison” messages keep being retried repeatedly and prevent other messages from being processed efficiently. Which SQS configuration most directly addresses this issue?
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 a redrive policy with a dead-letter queue (DLQ) and set an appropriate visibility timeout greater than the maximum processing time.
Option C is correct because configuring a redrive policy with a dead-letter queue (DLQ) allows messages that exceed a specified maximum receive count to be moved to the DLQ, isolating poison messages. Setting the visibility timeout greater than the maximum processing time ensures the consumer has enough time to process each message before it becomes visible again, preventing premature retries. This directly addresses the issue of poison messages blocking the queue and degrading throughput.
Key principle: A redrive policy links a source queue to a dead-letter queue (DLQ).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the SQS queue’s retention period to 10 years and rely on application retries to eventually succeed.
Why it's wrong here
Retention affects how long messages remain available, but it doesn’t isolate repeatedly failing messages.
- ✗
Increase visibility timeout to a very large value and avoid dead-letter queues to keep ordering stable.
Why it's wrong here
Long visibility time can delay failures, but it does not provide dead-letter isolation for poison messages.
- ✓
Configure a redrive policy with a dead-letter queue (DLQ) and set an appropriate visibility timeout greater than the maximum processing time.
Why this is correct
A DLQ isolates poison messages after a receive count threshold, and correct visibility timeout prevents premature retries.
Related concept
A redrive policy links a source queue to a dead-letter queue (DLQ).
- ✗
Switch the queue to FIFO and remove retries in the Lambda event source mapping entirely.
Why it's wrong here
FIFO and disabling retries do not reliably solve poison message isolation without DLQ-based redriving.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse increasing the visibility timeout or switching to FIFO as solutions for poison messages, but neither addresses the root cause of isolating messages that repeatedly fail processing.
Detailed technical explanation
How to think about this question
Under the hood, the redrive policy uses the `maxReceiveCount` parameter in the SQS queue's attributes; when a message is received more than this count, it is automatically moved to the configured DLQ. The visibility timeout must be set to a value that accounts for the maximum processing time of the Lambda function, including any retries within the function, to avoid the message being redelivered before processing completes. In real-world scenarios, a common mistake is setting the visibility timeout too low, causing duplicate processing and increased costs, while a DLQ ensures poison messages are isolated for manual inspection or separate handling.
KKey Concepts to Remember
- A redrive policy links a source queue to a dead-letter queue (DLQ).
- DLQs isolate messages that fail processing after a `maxReceiveCount` threshold.
- Visibility timeout prevents messages from being processed by multiple consumers simultaneously.
- The `maxReceiveCount` in the redrive policy determines how many times a message is retried before moving to the DLQ.
TExam Day Tips
- 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
A redrive policy links a source queue to a dead-letter queue (DLQ).
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. A redrive policy links a source queue to a dead-letter queue (DLQ). Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 |
What to study next
Got this wrong? Here's your next step.
Review a redrive policy links a source queue to a dead-letter queue (DLQ)., then practise related SAA-C03 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this SAA-C03 question test?
Design Resilient Architectures — This question tests Design Resilient Architectures — A redrive policy links a source queue to a dead-letter queue (DLQ)..
What is the correct answer to this question?
The correct answer is: Configure a redrive policy with a dead-letter queue (DLQ) and set an appropriate visibility timeout greater than the maximum processing time. — Option C is correct because configuring a redrive policy with a dead-letter queue (DLQ) allows messages that exceed a specified maximum receive count to be moved to the DLQ, isolating poison messages. Setting the visibility timeout greater than the maximum processing time ensures the consumer has enough time to process each message before it becomes visible again, preventing premature retries. This directly addresses the issue of poison messages blocking the queue and degrading throughput.
What should I do if I get this SAA-C03 question wrong?
Review a redrive policy links a source queue to a dead-letter queue (DLQ)., then practise related SAA-C03 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
A redrive policy links a source queue to a dead-letter queue (DLQ).
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Last reviewed: Jun 11, 2026
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