SAA-C03 Design Resilient Architectures Practice Question
An event-driven order processing service consumes messages from an Amazon SQS Standard queue. After a deployment, about 1% of messages start failing validation because a required field is missing. The consumer catches the exception and returns control, so the messages are retried. However, those poison messages keep reappearing and repeatedly consuming processing time for hours, delaying handling of valid messages. What is the most resilient way to handle the poison messages while keeping the system available?
⚠ Common exam trap
Watch out — candidates often think increasing visibility timeout or concurrency solves the problem, but they fail to recognize that only a dead-letter queue permanently isolates poison messages from the processing pipeline.
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 an SQS redrive policy to send messages to a dead-letter queue (DLQ) after a limited number of receives (maxReceiveCount).
Configuring an SQS redrive policy with a maxReceiveCount (e.g., 3–5) automatically moves messages that repeatedly fail processing to a dead-letter queue (DLQ) after the specified number of receives. This isolates the poison messages, preventing them from consuming visibility timeout and processing resources, while allowing valid messages to be handled without delay. The DLQ can then be analyzed or reprocessed offline, maintaining system availability.
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 consumer visibility timeout to a very large value so failing messages are hidden for hours.
Why it's wrong here
Setting the visibility timeout to a very large value only delays a poison message's reappearance; once the timeout expires, the message is delivered again and continues consuming receive/processing capacity. This does not create a retry limit or quarantine, so a persistent malformed message can still tie up resources and potentially impact the throughput of valid messages. Visibility timeout is designed to prevent other consumers from picking up a message during processing, not to remediate repeated failures.
- ✓
Configure an SQS redrive policy to send messages to a dead-letter queue (DLQ) after a limited number of receives (maxReceiveCount).
Why this is correct
A DLQ redrive policy creates a deterministic stop condition for poison messages. After maxReceiveCount, the messages are moved to the DLQ instead of cycling in the main queue, preventing repeated failed deliveries from degrading capacity and availability for valid messages.
- ✗
Switch the SQS queue from Standard to FIFO so poison messages do not retry.
Why it's wrong here
FIFO queues preserve ordering (and provide deduplication), but they do not eliminate retry behavior. Without a DLQ/redrive policy, poison messages can still be received and retried until the retry limit is reached by application logic or retention/processing patterns.
- ✗
Increase the consumer concurrency indefinitely so the system processes all messages even if some fail validation.
Why it's wrong here
Higher concurrency may improve throughput for a time, but it does not remove poison messages from the system. The poison messages can still consume CPU/network resources repeatedly and can increase contention, costs, and instability rather than isolating the failures.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAA-C03 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 SAA-C03 exam.