AI0-001 AI Implementation and Operations Practice Question
An AI system for fraud detection shows a gradual decline in precision over several weeks, though recall remains stable. Which type of model drift is most likely occurring?
⚠ Common exam trap
The CompTIA AI exam often tests the distinction between data drift and concept drift by presenting a scenario where only one performance metric changes, tempting candidates to incorrectly choose data drift because they associate any performance decline with input data changes, rather than recognizing that a stable recall with dropping precision points to a shift in the underlying concept.
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
✓
Concept drift
Concept drift occurs when the statistical relationship between input features and the target variable changes over time, causing the model's decision boundary to become less accurate. In this scenario, precision is declining while recall remains stable, indicating that the model is producing more false positives even though it still catches the same proportion of true positives. This is a classic sign of concept drift, where the underlying definition of fraud has shifted, not the data distribution itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data drift
Why it's wrong here
Data drift changes the distribution of input features while the input-to-label relationship holds, so precision and recall typically move together rather than precision alone degrading. It is tempting because data drift is the commonest drift form, and it is the right diagnosis when overall input distributions shift without label-conditional changes.
- ✗
Covariate shift
Why it's wrong here
Covariate shift is the same phenomenon as data drift — a change in P(X) with P(Y|X) fixed — so it cannot explain precision falling while recall holds. It is tempting because the term is precise and widely used, and it is correct when inputs shift but the decision boundary remains valid.
- ✗
Label drift
Why it's wrong here
Label drift changes P(Y|X), which alters the decision threshold's validity and typically moves precision and recall in opposite directions only when class priors shift, not gradually with recall fixed. It is tempting because fraud patterns do evolve, making label drift the right diagnosis when the meaning of the target itself changes.
- ✓
Concept drift
Why this is correct
Precision falling while recall stays stable indicates the relationship between input features and the fraud label has shifted, so previously correct positive predictions increasingly become false positives. That change in the underlying target concept, rather than input distribution, is concept drift.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.