MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?
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
Data quality monitoring in SageMaker Model Monitor detects schema and statistical drift (including distribution changes) for input features. Model quality monitors predictions vs. ground truth, not input features.
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 monitoring
Why it's wrong here
Bias drift monitoring detects changes in fairness metrics, not the distribution of individual input features.
- ✓
Data quality monitoring
Why this is correct
Data quality monitoring compares the distributions of input features against a baseline to detect statistical and schema drift.
- ✗
Model quality monitoring
Why it's wrong here
Model quality monitoring tracks metrics like accuracy or precision using ground truth labels, not input feature distributions.
- ✗
Feature attribution drift monitoring
Why it's wrong here
Feature attribution drift monitors changes in SHAP values over time, not the raw feature distributions.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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