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.
- ✗
SageMaker Pipelines
Why it's wrong here
SageMaker Pipelines orchestrates ML workflows as DAGs for automation and reproducibility; it has no drift-detection or baseline-comparison capability. It is tempting because Pipelines can schedule recurring jobs, and it would be correct when the requirement is building, automating and tracking a repeatable training or deployment workflow.
- ✓
SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor continuously evaluates endpoint input data against a training baseline, computing statistical distances to raise CloudWatch alerts when drift exceeds thresholds. This directly satisfies the requirement for automatic detection of input distribution shifts, unlike Model Registry or Clarify, which address governance and bias respectively.
- ✗
SageMaker Debugger
Why it's wrong here
SageMaker Debugger captures tensors and analyses training jobs for convergence issues such as vanishing gradients or overfitting; it does not monitor deployed inference traffic. It is tempting because Debugger also produces metrics and alerts, and it would be correct when diagnosing problems inside a running training job.
- ✗
SageMaker Clarify
Why it's wrong here
SageMaker Clarify computes feature attribution and bias metrics, plus explainability reports, rather than continuously comparing live input distributions against a training baseline. It is tempting because Clarify also surfaces feature-level statistics, and it would be correct when the requirement is explaining predictions or detecting bias in training and inference data.
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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.