- A
SageMaker Clarify
Why wrong: Clarify helps detect bias and provide explanations, not data drift.
- B
SageMaker Debugger
Why wrong: Debugger monitors training, not deployed models.
- C
SageMaker Model Monitor
Model Monitor can track input data distributions and alert on drift.
- D
SageMaker Feature Store
Why wrong: Feature Store manages features, not monitoring for drift.
Question 584 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A model deployed on SageMaker is returning…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 SageMaker is returning inaccurate predictions for certain customer segments. The team suspects data drift. Which SageMaker feature should they use to continuously monitor 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
✓
SageMaker Model Monitor
SageMaker Model Monitor is the correct choice because it is specifically designed to continuously monitor the input data distribution of a deployed model and detect data drift over time. It automatically captures and analyzes the statistical properties of incoming inference requests against a baseline, alerting you when significant deviations occur.
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.
- ✗
SageMaker Clarify
Why it's wrong here
Clarify helps detect bias and provide explanations, not data drift.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger monitors training, not deployed models.
- ✓
SageMaker Model Monitor
Why this is correct
Model Monitor can track input data distributions and alert on drift.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Feature Store
Why it's wrong here
Feature Store manages features, not monitoring for drift.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse SageMaker Clarify's bias detection capabilities with data drift monitoring, but Clarify analyzes static datasets for fairness and explainability, not continuous production data distribution shifts.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor works by establishing a baseline of the training data distribution using statistical tests like the Kolmogorov-Smirnov test for numerical features and chi-squared test for categorical features. It then runs scheduled monitoring jobs that compare the distribution of live inference data against this baseline, and can automatically send alerts via CloudWatch when drift is detected, enabling proactive model retraining.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: SageMaker Model Monitor — SageMaker Model Monitor is the correct choice because it is specifically designed to continuously monitor the input data distribution of a deployed model and detect data drift over time. It automatically captures and analyzes the statistical properties of incoming inference requests against a baseline, alerting you when significant deviations occur.
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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