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ML Solution Monitoring, Maintenance, and SecuritymediumMultiple ChoiceObjective-mapped

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

A data science team uses Amazon SageMaker Model Monitor to detect data drift in production. They notice that the schema of incoming data (number of features) has changed compared to the training baseline. Which type of monitor is BEST suited to detect this issue?

⚠ Common exam trap

A common mix-up: candidates confuse 'data drift' (distribution shift) with 'schema change' and incorrectly choose Feature attribution drift monitor, thinking it covers all input changes, but it only tracks importance shifts, not structural feature count violations.

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

Data quality monitor

The Data quality monitor in SageMaker Model Monitor is specifically designed to detect violations in the input data schema, such as changes in the number of features, feature types, or missing values, by comparing incoming data against a baseline computed from the training dataset. Since the issue is a structural change in the schema (number of features), the Data quality monitor is the correct choice.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Bias drift monitor

    Why it's wrong here

    Bias drift monitor checks for fairness metric changes over time, not schema changes.

  • Feature attribution drift monitor

    Why it's wrong here

    Feature attribution drift monitor uses SHAP to detect changes in feature importance, not schema.

  • Data quality monitor

    Why this is correct

    Data quality monitor checks for schema violations and statistical drift in input features.

  • Model quality monitor

    Why it's wrong here

    Model quality monitor checks prediction accuracy against ground truth, not input schema.

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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.