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 tracks changes in which features drive predictions, not shifts in input data distribution, so it cannot proxy for accuracy loss when labels are absent. It is tempting because it detects model behaviour changes, and would be correct if the concern were explainability or feature-importance stability rather than data drift.
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Model quality drift
Why it's wrong here
Model quality drift measures accuracy against ground truth labels, which the scenario states are unavailable, so it cannot be computed directly. It suits monitored deployments where labels arrive promptly and prediction accuracy can be tracked over time.
- ✓
Data drift
Why this is correct
Without ground truth labels, accuracy cannot be measured directly, so data drift is monitored as a proxy: it compares the live input feature distribution against the training baseline. A significant divergence signals that the model is operating on data unlike what it learned from, indicating likely degradation.
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Concept drift
Why it's wrong here
Concept drift describes the changing relationship between inputs and the target, but detecting it requires labelled outcomes to confirm the mapping shifted. It suits scenarios with timely ground truth; here, without labels, data drift in inputs serves as the practical proxy.
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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.