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.)
⚠ Common exam trap
The trap is assuming a single monitoring approach covers both drift types; candidates often pick Clarify or Debugger because they sound like monitoring tools, but only Model Monitor's data quality and model quality modes address data and concept drift respectively.
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
Option A is correct because SageMaker Model Monitor's data quality monitoring detects data drift by comparing the statistical properties of incoming inference requests against the baseline statistics captured from the training dataset, alerting when feature distributions shift. Option D is correct because Model Monitor's model quality monitoring evaluates concept drift by comparing predicted values against actual ground-truth labels, tracking metrics such as accuracy, precision, and recall over time to detect degradation in the model's real-world performance. Option B is not correct because SageMaker Clarify is used for bias detection and explainability (e.g., SHAP values), not for detecting data or concept drift. Option C is not correct because CloudWatch Logs Insights merely queries and analyzes log data; it does not provide built-in drift detection statistics or baseline comparisons. Option E is not correct because SageMaker Debugger is designed to debug training jobs by capturing tensors and monitoring training metrics, not to monitor deployed models for 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 for data quality monitoring
Why this is correct
Data quality monitoring compares live inference inputs against the training baseline, computing per-feature distribution statistics to detect data drift — changes in input feature distributions. This directly satisfies the stem's data drift requirement, while concept drift needs the separate model quality monitor.
- ✗
Use SageMaker Clarify for bias monitoring
Why it's wrong here
SageMaker Clarify detects bias in data and model predictions, not drift between distributions over time. It is tempting because Clarify also computes feature attribution and statistical metrics, making it the right choice when a team must audit fairness across protected groups before or after deployment, rather than track drift.
- ✗
Configure CloudWatch Logs Insights to query inference logs
Why it's wrong here
CloudWatch Logs Insights queries and aggregates log fields, but it neither computes distribution comparisons nor triggers drift alarms; it lacks a baseline model. It is tempting because it inspects inference logs, which suits ad-hoc troubleshooting of errors or latency, not statistical drift detection.
- ✓
Set up SageMaker Model Monitor for model quality monitoring
Why this is correct
Model quality monitoring compares predictions against ground-truth labels to measure accuracy decay, which is how concept drift manifests — the input-to-output relationship shifting. This satisfies the stem's concept drift requirement, complementing data quality monitoring for data drift.
- ✗
Enable SageMaker Debugger during inference
Why it's wrong here
SageMaker Debugger captures tensors and metrics during training jobs, not production inference, so it cannot detect drift in live traffic. It is tempting because it monitors model internals, which suits debugging training convergence or vanishing gradients rather than comparing deployed input distributions against baselines.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.