Quick Answer
The answer is Amazon SageMaker Model Monitor. This service is specifically designed to detect data drift by continuously capturing and analyzing the statistical distribution of input features at the endpoint, comparing them against a baseline to flag significant deviations. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between monitoring services: CloudWatch Logs handles raw log storage, CloudTrail audits API calls, and Athena queries data in S3, but only Model Monitor automates the statistical comparison of live inference data against a training baseline. A common trap is confusing CloudWatch for drift detection, but remember that CloudWatch is for operational metrics, not feature distribution analysis. Memory tip: think “Model Monitor = Model’s Input Monitor” to recall its focus on input feature distributions rather than infrastructure logs.
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 data science team deploys a real-time inference endpoint on Amazon SageMaker. They want to monitor for data drift in the input features over time. Which AWS service should they use to capture and analyze the input data distribution?
Amazon Athena
Why wrong: Athena is for querying data in S3, not automated drift monitoring.
AWS CloudTrail
Why wrong: CloudTrail records API calls, not data distributions.
Amazon SageMaker Model Monitor
Model Monitor captures input data and computes statistics to detect drift.
Amazon CloudWatch Logs
Why wrong: CloudWatch Logs is for storing logs, not for data drift detection.
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 Model Monitor
Amazon SageMaker Model Monitor is designed to detect data drift by capturing and analyzing input data distributions. CloudWatch Logs is for logging, CloudTrail for API auditing, and Athena for ad-hoc querying.
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.
Amazon Athena
Why it's wrong here
Athena is for querying data in S3, not automated drift monitoring.
AWS CloudTrail
Why it's wrong here
CloudTrail records API calls, not data distributions.
Amazon SageMaker Model Monitor
Why this is correct
Model Monitor captures input data and computes statistics to detect drift.
Related concept
Read the scenario before looking for a memorised answer.
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs is for storing logs, not for data drift detection.
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 Model Monitor — Amazon SageMaker Model Monitor is designed to detect data drift by capturing and analyzing input data distributions. CloudWatch Logs is for logging, CloudTrail for API auditing, and Athena for ad-hoc querying.
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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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data science team deploys a machine learning model to a SageMaker endpoint for real-time inference. They need to monitor the model for feature distribution drift over time to ensure the model's predictions remain accurate. Which AWS service should they use?
easyWhy D: SageMaker Model Monitor is specifically designed to detect drift in feature distributions and prediction quality over time.
Variation 2. A team deploys a model with SageMaker and notices that the model returns inconsistent results during inference. They suspect a mismatch in feature transformation between the training pipeline and the inference pipeline. Which SageMaker feature can help compare the feature distributions?
mediumWhy A: Option B is correct because SageMaker Model Monitor can track feature distributions over time and detect drift. Option A is for debugging training jobs. Option C is for explainability and bias detection. Option D is for automated model building.
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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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