AI Associate AI Fundamentals Practice Question
A company uses an AI model to classify customer support cases into categories (billing, technical, general). The model performs well on training data but poorly on new cases. Which issue is MOST likely occurring?
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
✓
Overfitting
Overfitting means the model memorized the training data and fails to generalize to new, unseen data. Underfitting would show poor performance on both training and test data. Data leakage occurs when future information leaks into training. Bias in data is a different issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Underfitting
Why it's wrong here
Underfitting would cause poor performance even on training data, not just on new cases.
- ✓
Overfitting
Why this is correct
Overfitting leads to high accuracy on training data but low accuracy on new data because the model is too complex.
- ✗
Data leakage
Why it's wrong here
Data leakage would give the model unrealistic access to future information, often causing overly optimistic training performance, but not necessarily poor generalization.
- ✗
Bias in training data
Why it's wrong here
Bias in data might lead to systematic errors but not specifically the symptom of high training accuracy and low test accuracy.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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