MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company uses SageMaker Model Monitor to detect data drift in production. The monitoring job compares the current data distribution to a baseline. Which TWO types of drift can SageMaker Model Monitor detect? (Select TWO)
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 drift (schema and statistical drift)
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Concept drift (change in the relationship between features and target)
Why it's wrong here
Concept drift is not directly detected by Model Monitor; it is inferred from model quality drift.
- ✓
Data quality drift (schema and statistical drift)
Why this is correct
Model Monitor can detect schema violations and statistical distribution changes.
- ✓
Model quality drift (performance degradation against ground truth)
Why this is correct
Model quality drift is detected when ground truth labels become available.
- ✗
Bias drift (change in bias metrics over time)
Why it's wrong here
Bias drift monitoring requires SageMaker Clarify, not Model Monitor alone.
- ✗
Feature attribution drift (SHAP values)
Why it's wrong here
Feature attribution drift requires Clarify's explainability monitoring.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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