MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team wants to detect concept drift in a production model. Which TWO actions should they take? (Choose 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
✓
Enable data capture on the endpoint to collect ground truth labels
Concept drift is detected by comparing model predictions to actual outcomes (ground truth). Capturing ground truth and using model quality monitoring is essential. Data quality monitoring would not detect concept 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.
- ✗
Set up SageMaker Model Monitor data quality monitoring schedule
Why it's wrong here
Data quality monitors input features, not concept drift.
- ✗
Use SageMaker Clarify for feature attribution drift
Why it's wrong here
Feature attribution drift is different from concept drift.
- ✗
Enable daily retraining to automatically correct drift
Why it's wrong here
Retraining is a response, not a detection action.
- ✓
Enable data capture on the endpoint to collect ground truth labels
Why this is correct
Ground truth labels are needed for comparison.
- ✓
Set up SageMaker Model Monitor model quality monitoring schedule
Why this is correct
Model quality monitoring compares predictions to ground truth.
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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.