MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team needs to monitor a deployed model for both data drift and concept drift. Which TWO approaches should they implement? (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
✓
Set up SageMaker Model Monitor for data quality monitoring
SageMaker Model Monitor can be configured for data quality (data drift) and model quality (concept drift) monitoring. Data drift monitors input distribution changes, while model quality monitors prediction accuracy against ground truth.
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 for data quality monitoring
Why this is correct
Data quality monitoring detects drift in input features.
- ✗
Use SageMaker Clarify for bias monitoring
Why it's wrong here
Clarify monitors bias, not drift.
- ✗
Configure CloudWatch Logs Insights to query inference logs
Why it's wrong here
Logs Insights can analyze logs but does not provide automated drift detection.
- ✓
Set up SageMaker Model Monitor for model quality monitoring
Why this is correct
Model quality monitoring detects drift in prediction performance.
- ✗
Enable SageMaker Debugger during inference
Why it's wrong here
Debugger is for training, not inference.
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JA
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.