AI0-001 AI Implementation and Operations Practice Question
A financial institution deploys an AI credit scoring model. After six months, the model's performance drops significantly. Analysis shows that the relationship between features and labels has changed. Which term describes this phenomenon?
⚠ Common exam trap
CompTIA often tests the distinction between concept drift and data drift, and the trap here is that candidates confuse a change in input data distribution (data drift) with a change in the underlying relationship between features and labels (concept drift), leading them to incorrectly select data drift.
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 label changes over time, which is exactly what happened when the credit scoring model's performance dropped due to a shift in the feature-label relationship. This is distinct from data drift, which only involves changes in the input data distribution without affecting the label mapping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Concept drift
Why this is correct
Concept drift directly refers to changes in the relation between inputs and outputs.
- ✗
Model decay
Why it's wrong here
Model decay is a symptom, not the specific cause.
- ✗
Overfitting
Why it's wrong here
Overfitting is a training-time problem.
- ✗
Data drift
Why it's wrong here
Data drift changes input distribution, not necessarily the target relationship.
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