mediumMultiple Select
MLA-C01 Amazon SageMaker Model Monitor Practice Question
A team deploys a machine learning model using an Amazon SageMaker endpoint. They need to monitor for data drift and model quality issues. Which AWS services or features should they use? (Choose THREE.)
⚠ Common exam trap
The trap is confusing SageMaker Clarify (bias/explainability) with SageMaker Model Monitor (data drift/quality), as both involve analyzing data distributions, but Clarify focuses on bias and attributions, while Model Monitor handles drift detection.
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
✓
Amazon SageMaker Clarify
Amazon SageMaker Model Monitor (E) is the purpose-built feature for continuously monitoring deployed SageMaker endpoints, detecting data drift, model quality degradation, bias drift, and feature attribution drift by comparing live traffic against a baseline. Amazon SageMaker Clarify (B) provides bias detection and explainability (SHAP-based feature attributions), and its bias/explainability metrics are integrated with Model Monitor to surface those drift and fairness issues. Amazon CloudWatch Logs and Metrics (D) capture the endpoint's invocation logs and metrics that Model Monitor analyzes and that trigger CloudWatch alarms when violations are detected. AWS Glue DataBrew (A) is a visual data preparation tool for cleaning and transforming datasets, not for monitoring live endpoints, and SageMaker Ground Truth (C) is a data labeling service for building training datasets, so neither addresses drift or model quality monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Glue DataBrew
Why it's wrong here
DataBrew profiles and transforms datasets for preparation; it does not monitor live endpoint inference for drift or quality. It tempts because it inspects data distributions, but that is pre-training preparation, whereas production monitoring needs Model Monitor, Clarify and CloudWatch.
- ✓
Amazon SageMaker Clarify
Why this is correct
SageMaker Clarify detects bias and explains feature attributions, and its drift monitoring integrates with Model Monitor to compare live endpoint traffic against a baseline. This directly satisfies the stem's data drift and model quality monitoring requirement for the deployed SageMaker endpoint.
- ✗
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth labels training data; it does not monitor a deployed endpoint for drift or quality. It tempts because it supports human review workflows, but that is data labelling and model evaluation during development, whereas drift detection requires SageMaker Model Monitor and Clarify.
- ✓
Amazon CloudWatch Logs and Metrics
Why this is correct
CloudWatch Logs and Metrics capture endpoint invocation logs, latency and error metrics, providing the underlying telemetry that drift and quality monitoring depend on. This satisfies the scenario's need to monitor a SageMaker endpoint for data drift and model quality issues.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor continuously evaluates endpoint data against baselines, detecting data drift, model quality degradation, bias drift and feature attribution drift. It directly satisfies the scenario's requirement to monitor a SageMaker endpoint for data drift and model quality issues.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.