MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning engineer wants to monitor a deployed model for data drift. Which SageMaker feature should they use to automatically detect drift in the input data distribution compared to the training data baseline?
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 can be configured to run monitoring jobs that compare live inference data against a baseline created from training data to detect data drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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SageMaker Pipelines
Why it's wrong here
SageMaker Pipelines is for building and deploying ML workflows, not monitoring drift.
- ✓
SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor provides data quality monitoring to detect drift in input data distributions.
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SageMaker Debugger
Why it's wrong here
SageMaker Debugger is for debugging training jobs, not monitoring deployed endpoints.
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SageMaker Clarify
Why it's wrong here
SageMaker Clarify focuses on bias detection and explainability, not continuous data drift monitoring.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.