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 tracks disparity in predictions across sensitive groups such as age or sex, not the number of incoming features. It is tempting as a fairness safeguard, and would be correct when a model's disparate impact against a baseline needs continuous production measurement.
- ✗
Feature attribution drift monitor
Why it's wrong here
Feature attribution drift compares the contribution of each feature to predictions against the baseline, so an added or removed feature is not its target. It is tempting because it is a drift monitor, and would be correct when feature importance rankings shift while the schema stays fixed.
- ✓
Data quality monitor
Why this is correct
Data quality monitoring compares incoming data statistics and schema against the training baseline, so a changed feature count is flagged as a schema violation. Model quality and bias monitors assess predictions, not input structure, making them unsuitable here.
- ✗
Model quality monitor
Why it's wrong here
Model quality monitor compares prediction accuracy against ground-truth labels, which requires labelled outcomes and says nothing about feature count. It is tempting because it detects degradation, and would be correct when accuracy or regression error drifts after labels arrive.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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