MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
An ML engineer monitors a SageMaker endpoint for data drift. They set up SageMaker Model Monitor to compare inference data against a baseline created from the training dataset. The monitoring schedule runs daily and reports violations. Which monitoring type should be configured to detect if the distribution of a numerical feature in real-time inference data differs significantly from the training distribution?
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 monitoring
SageMaker Model Monitor's data quality monitoring detects feature distribution drift (statistical drift) between baseline and live data. Model quality monitoring requires ground truth labels, bias drift monitors fairness metrics, and feature attribution drift monitors SHAP values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data quality monitoring
Why this is correct
Data quality monitoring compares the statistical distribution of features in captured inference data against a baseline built from the training dataset, detecting drift in numerical features. This matches the stem's requirement to flag significant distribution differences.
- ✗
Feature attribution drift monitoring
Why it's wrong here
Feature attribution drift monitoring tracks changes in the relative importance of input features using explainability baselines, not the raw distribution of a numerical feature. It is tempting because it also uses a training baseline, and would be correct when you need to detect that the model now relies on different features than during training.
- ✗
Bias drift monitoring
Why it's wrong here
Bias drift monitoring compares predicted label distributions across sensitive groups against a bias baseline, so it measures fairness metrics rather than a numerical feature's distribution. It is tempting because it also runs on a schedule against a baseline, and would be correct when regulatory fairness thresholds across demographic groups must be tracked.
- ✗
Model quality monitoring
Why it's wrong here
Model quality monitoring compares predictions against ground-truth labels to measure accuracy metrics, so it cannot detect a shift in a numerical feature's distribution. It is tempting because it is the default Model Monitor type, and would be correct when labelled outcomes exist and you need to track precision, recall or MAE degradation.
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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.