- A
Amazon Athena
Why wrong: Athena is for querying data in S3, not automated drift monitoring.
- B
AWS CloudTrail
Why wrong: CloudTrail records API calls, not data distributions.
- C
Amazon SageMaker Model Monitor
Model Monitor captures input data and computes statistics to detect drift.
- D
Amazon CloudWatch Logs
Why wrong: CloudWatch Logs is for storing logs, not for data drift detection.
Question 431 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
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?
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
✓
Amazon SageMaker Model Monitor
Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor machine learning models in production for data drift and quality issues. It automatically captures input data distributions from real-time inference endpoints and compares them against a baseline to detect statistical changes, alerting the team when drift occurs.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse general logging and monitoring services (CloudWatch Logs, CloudTrail) with the specialized model monitoring service, overlooking that SageMaker Model Monitor provides built-in statistical drift detection rather than just raw log storage.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor works by deploying a monitoring schedule that captures inference requests and responses from the endpoint, then runs statistical tests (e.g., Kolmogorov-Smirnov test for continuous features, chi-squared test for categorical features) against a baseline dataset. It outputs constraint violations and drift reports to Amazon S3 and CloudWatch, enabling automated retraining triggers. A subtle behavior is that it requires a baseline dataset to be computed from training data or an initial period of inference data, and drift detection is sensitive to the chosen threshold (e.g., p-value < 0.05).
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. 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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon SageMaker Model Monitor — Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor machine learning models in production for data drift and quality issues. It automatically captures input data distributions from real-time inference endpoints and compares them against a baseline to detect statistical changes, alerting the team when drift occurs.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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