easyMultiple Choice
MLA-C01 Practice Question: A data science team deploys a machine learning…
A data science team deploys a machine learning model to a SageMaker endpoint for real-time inference. They need to monitor the model for feature distribution drift over time to ensure the model's predictions remain accurate. Which AWS service should they use?
⚠ Common exam trap
Candidates often confuse SageMaker Model Monitor with SageMaker Clarify or Debugger, as candidates often misattribute drift monitoring to Clarify's bias detection or Debugger's training-time analysis, but only Model Monitor handles post-deployment feature drift.
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 service because it is specifically designed to continuously monitor machine learning models deployed to SageMaker endpoints for data quality issues, including feature distribution drift. It automatically captures inference data, computes statistics against a baseline, and triggers alerts when drift is detected, ensuring the model's predictions remain accurate over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon CloudWatch Evidently
Why it's wrong here
Evidently runs experiments and compares variant model performance, not continuous feature distribution monitoring on a live endpoint. It tempts because it also handles production ML traffic, but it would be correct for A/B testing model variants rather than detecting drift in input feature distributions.
- ✗
AWS Glue DataBrew
Why it's wrong here
DataBrew profiles and transforms datasets for preparation, not monitoring deployed endpoints for drift. It tempts because it surfaces distribution statistics during profiling, but it would be correct for cleaning and normalising training data, not for continuous feature drift detection on live inference traffic.
- ✗
SageMaker Clarify
Why it's wrong here
Clarify explains predictions and detects bias in models, not ongoing feature distribution drift. It tempts because it analyses model behaviour, but it would be correct for bias reporting and feature attribution at training or endpoint level, not for tracking distribution shifts over time.
- ✓
SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor directly satisfies the requirement to detect feature distribution drift on a live endpoint. It captures real-time inference request data, computes baseline statistics, and compares them against production traffic using configurable drift thresholds, emitting CloudWatch metrics and alerts when divergence is detected.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger captures training-job tensors and metrics to diagnose convergence issues, not post-deployment feature drift. It tempts because it monitors model internals, but it would be correct during training to find vanishing gradients or overfitting, not on a live endpoint serving inference.
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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.