MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company deploys a model for fraud detection. They want to monitor if the model's predictions become less accurate over time due to changes in the underlying data distribution, but they do not have immediate access to ground truth labels. Which type of drift should they monitor as a proxy?
⚠ Common exam trap
AWS often tests the distinction between data drift and concept drift, and the trap here is that candidates confuse 'changes in data distribution' (data drift) with 'changes in the relationship between features and labels' (concept drift), assuming both require labels when only concept drift does.
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 drift
Data drift (option C) is the correct proxy to monitor when ground truth labels are unavailable because it detects changes in the input feature distribution over time. If the underlying data distribution shifts, the model's predictions are likely to become less accurate even if the relationship between features and labels remains stable. This allows teams to trigger retraining or investigation before model quality degrades.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature attribution drift
Why it's wrong here
Feature attribution drift monitors SHAP values; while it can indicate changes, it is often used for concept drift detection with labels.
- ✗
Model quality drift
Why it's wrong here
Model quality drift requires ground truth labels to compute metrics like accuracy or F1-score.
- ✓
Data drift
Why this is correct
Data drift (input distribution change) can be monitored without labels; significant data drift may indicate potential concept drift.
- ✗
Concept drift
Why it's wrong here
Concept drift detection typically requires ground truth labels to compare prediction accuracy over time; without labels, it is difficult to measure.
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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.