This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 team deploys a machine learning model using an Amazon SageMaker endpoint. They need to monitor for data drift and model quality issues. Which AWS services or features should they use? (Choose THREE.)
AWS Glue DataBrew
Why wrong: Data preparation service.
Amazon SageMaker Clarify
Provides bias and explainability monitoring.
Amazon SageMaker Ground Truth
Why wrong: Used for data labeling.
Amazon CloudWatch Logs and Metrics
For storing monitoring outputs and setting alarms.
Amazon SageMaker Model Monitor
Provides data drift and model quality monitoring.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker Clarify
Options A, C, and E are correct. A: SageMaker Model Monitor can monitor data drift and model quality. C: SageMaker Clarify can monitor bias and feature attribution drift. E: Amazon CloudWatch can collect custom metrics and set alarms, used with Model Monitor. Option B is wrong because SageMaker Ground Truth is for labeling, not monitoring. Option D is wrong because AWS Glue is for ETL, not monitoring deployed models.
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.
AWS Glue DataBrew
Why it's wrong here
Data preparation service.
Amazon SageMaker Clarify
Why this is correct
Provides bias and explainability monitoring.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker Ground Truth
Why it's wrong here
Used for data labeling.
Amazon CloudWatch Logs and Metrics
Why this is correct
For storing monitoring outputs and setting alarms.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker Model Monitor
Why this is correct
Provides data drift and model quality monitoring.
Related concept
Read the scenario before looking for a memorised answer.
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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Amazon SageMaker Clarify — Options A, C, and E are correct. A: SageMaker Model Monitor can monitor data drift and model quality. C: SageMaker Clarify can monitor bias and feature attribution drift. E: Amazon CloudWatch can collect custom metrics and set alarms, used with Model Monitor. Option B is wrong because SageMaker Ground Truth is for labeling, not monitoring. Option D is wrong because AWS Glue is for ETL, not monitoring deployed models.
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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