AI0-001 AI Concepts and Foundations Practice Question
An AI team notices that their model's performance degrades over time because the statistical relationship between input features and the target variable changes. This issue is called:
⚠ Common exam trap
CompTIA often tests the distinction between data drift and concept drift, where candidates mistakenly choose data drift because they focus on the input features changing, rather than the relationship between features and the target.
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 model performance to degrade. This is distinct from data drift, which involves changes in the input data distribution alone. In the AI0-001 context, concept drift directly addresses the shift in the underlying mapping from features to labels.
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 describes a change in the input feature distribution, P(X), while the input-to-target relationship P(Y|X) stays fixed. The stem specifies that relationship changing, which is concept drift; data drift would be the answer if only the inputs shifted.
- ✗
Overfitting
Why it's wrong here
Overfitting is a training-time phenomenon where the model memorises training data and generalises poorly from the outset, not a temporal degradation after deployment. It is addressed with regularisation or more data, not by monitoring live input distributions.
- ✓
Concept drift
Why this is correct
Concept drift is the change in the statistical relationship between input features and the target variable over time, degrading model performance. This matches the stem exactly, unlike data drift, which shifts input distributions while the underlying mapping stays constant.
- ✗
Model drift
Why it's wrong here
Model drift is an umbrella term covering any degradation in deployed model performance, including data drift and concept drift. The stem names the specific mechanism — a changed input-to-target relationship — which is concept drift, so the general label is imprecise.
About these practice questions
One of 962 original AI0-001 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.