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MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security

A company has deployed a machine learning model on Amazon SageMaker and wants to automatically detect when the distribution of input features deviates significantly from the training data distribution. Which SageMaker feature should they use?

⚠ Common exam trap

Candidates often confuse 'Data Quality Monitoring' with 'Model Quality Monitoring', mistakenly thinking that monitoring prediction accuracy covers input distribution drift, whereas Data Quality Monitoring is explicitly for input features and Model Quality Monitoring is for output predictions.

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 – Data Quality Monitoring

SageMaker Model Monitor – Data Quality Monitoring is the correct choice because it is specifically designed to detect deviations in the distribution of input features compared to the training data distribution. It continuously monitors incoming inference requests and compares statistical properties (e.g., mean, variance, or histogram) against a baseline computed from the training dataset, alerting when drift is detected.

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

    SageMaker Clarify is used for bias detection and explainability, not data drift detection.

  • SageMaker Edge Manager

    Why it's wrong here

    Edge Manager is for managing models on edge devices, not for monitoring data drift.

  • SageMaker Model Monitor – Model Quality Monitoring

    Why it's wrong here

    Model quality monitoring tracks prediction accuracy against ground truth, not input data drift.

  • SageMaker Model Monitor – Data Quality Monitoring

    Why this is correct

    Data quality monitoring detects schema drift and statistical drift by comparing live data to a baseline.

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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.