Quick Answer
The answer is to enable SageMaker endpoint data capture to the S3 bucket. This is the most efficient method because SageMaker’s built-in data capture feature is specifically designed to automatically log predictions and input payloads directly to a specified S3 location without requiring any custom inference code or additional infrastructure. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of operational best practices for model monitoring and auditing, often appearing as a trap where candidates overcomplicate the solution by suggesting custom logging in the inference script or routing through Kinesis Firehose. Remember, SageMaker data capture is a native, zero-code configuration toggle on the endpoint—it captures both request and response payloads, making it far simpler and more reliable than manual logging. A helpful memory tip: think of “capture” as the one-click audit trail, not “code” or “complex pipelines.”
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 model deployed on a SageMaker endpoint is returning predictions. The team wants to log all predictions to an S3 bucket for auditing. What is the most efficient way to achieve this?
Enable SageMaker endpoint data capture to the S3 bucket.
Data capture is built-in and efficient.
Configure CloudWatch Logs to export to S3.
Why wrong: CloudWatch Logs is for logs, not prediction data.
Modify the inference code to write logs to S3.
Why wrong: Custom code requires maintenance and adds latency.
Use Amazon Kinesis Data Firehose to stream predictions to S3.
Why wrong: Firehose adds unnecessary complexity for simple logging.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Enable SageMaker endpoint data capture to the S3 bucket.
SageMaker data capture is designed for this purpose and can be enabled on the endpoint configuration to automatically capture input and output data to S3. Modifying inference code is custom and less efficient, Firehose adds complexity, and CloudWatch Logs export is for logs.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Enable SageMaker endpoint data capture to the S3 bucket.
Why this is correct
Data capture is built-in and efficient.
Related concept
Read the scenario before looking for a memorised answer.
Configure CloudWatch Logs to export to S3.
Why it's wrong here
CloudWatch Logs is for logs, not prediction data.
Modify the inference code to write logs to S3.
Why it's wrong here
Custom code requires maintenance and adds latency.
Use Amazon Kinesis Data Firehose to stream predictions to S3.
Why it's wrong here
Firehose adds unnecessary complexity for simple logging.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Identify which MLA-C01 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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FAQ
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
The correct answer is: Enable SageMaker endpoint data capture to the S3 bucket. — SageMaker data capture is designed for this purpose and can be enabled on the endpoint configuration to automatically capture input and output data to S3. Modifying inference code is custom and less efficient, Firehose adds complexity, and CloudWatch Logs export is for logs.
Identify which MLA-C01 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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
This MLA-C01 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 MLA-C01 exam.
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