hardMultiple Choice
MLA-C01 Practice Question: A model deployed on SageMaker is returning…
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?
⚠ Common exam trap
Watch out — 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.
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.
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 detects bias and explains predictions; it does not continuously monitor input distributions. It is tempting because it inspects model fairness and feature attribution, but the requirement is drift detection, which SageMaker Model Monitor performs by comparing captured endpoint data to a baseline.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger captures training tensors and metrics for jobs, not production input distributions. It is tempting because it monitors model behaviour, but drift detection requires SageMaker Model Monitor, which compares live endpoint data against a baseline and raises violations.
- ✓
SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor continuously captures endpoint input data and compares its distribution against a baseline, detecting drift via statistical tests such as KL divergence or L-infinity distance. This directly satisfies the requirement to monitor input data distribution across customer segments, triggering CloudWatch alerts when violations exceed thresholds.
- ✗
SageMaker Feature Store
Why it's wrong here
Feature Store centralises feature storage and serving for training and inference; it does not monitor drift. It is tempting because it holds the input features, but continuous distribution monitoring belongs to SageMaker Model Monitor, which analyses endpoint-captured data against a baseline.
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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.