- A
SageMaker Model Monitor with data quality monitoring.
Why wrong: Data quality monitoring checks statistical properties of input data, not bias.
- B
SageMaker Debugger to capture tensors.
Why wrong: Debugger is for training internals, not bias monitoring.
- C
SageMaker Ground Truth for fairness labels.
Why wrong: Ground Truth is for creating labeled datasets, not monitoring.
- D
SageMaker Clarify with bias drift detection.
Clarify can detect bias in predictions and attributes.
Question 207 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. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 financial services company is deploying a model for loan approval. They must ensure that the model's predictions do not show bias against protected groups. They plan to monitor for bias drift after deployment. Which SageMaker feature should they use?
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 Clarify with bias drift detection.
SageMaker Clarify is the correct choice because it provides built-in bias detection and monitoring capabilities, including the ability to detect bias drift over time after deployment. It can analyze predictions for protected groups and generate reports on metrics like disparate impact and conditional demographic disparity, which directly addresses the requirement to monitor for bias drift post-deployment.
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 Model Monitor with data quality monitoring.
Why it's wrong here
Data quality monitoring checks statistical properties of input data, not bias.
- ✗
SageMaker Debugger to capture tensors.
Why it's wrong here
Debugger is for training internals, not bias monitoring.
- ✗
SageMaker Ground Truth for fairness labels.
Why it's wrong here
Ground Truth is for creating labeled datasets, not monitoring.
- ✓
SageMaker Clarify with bias drift detection.
Why this is correct
Clarify can detect bias in predictions and attributes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between data quality monitoring (which tracks input data drift) and bias drift monitoring (which tracks fairness in predictions), leading candidates to mistakenly choose SageMaker Model Monitor when the question specifically asks about bias.
Detailed technical explanation
How to think about this question
SageMaker Clarify's bias drift detection works by comparing the distribution of predictions or features against a baseline dataset, using statistical measures such as the difference in positive proportions (DPPL) or the equal opportunity difference. It can be scheduled to run periodically on batch transforms or endpoints, and it outputs reports that highlight whether bias metrics have exceeded configurable thresholds. In a real-world scenario, a loan approval model might show increasing bias against a protected group over time due to changing economic conditions, and Clarify would alert the team to retrain or adjust the model.
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: SageMaker Clarify with bias drift detection. — SageMaker Clarify is the correct choice because it provides built-in bias detection and monitoring capabilities, including the ability to detect bias drift over time after deployment. It can analyze predictions for protected groups and generate reports on metrics like disparate impact and conditional demographic disparity, which directly addresses the requirement to monitor for bias drift post-deployment.
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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