AI Associate AI Fundamentals Practice Question
A data scientist trains a model to predict customer churn. The model performs well on training data but poorly on test data. Which TWO issues are most likely?
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 training data and fails to generalize. Data leakage inflates training performance but not test performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Overfitting
Why this is correct
Model fits noise in training data, leading to poor generalization.
- ✗
Insufficient training data
Why it's wrong here
Insufficient data typically causes poor performance on both sets, not just test.
- ✗
High bias
Why it's wrong here
High bias leads to underfitting, not overfitting.
- ✗
Underfitting
Why it's wrong here
Underfitting causes poor performance on both training and test sets.
- ✓
Data leakage
Why this is correct
If test information leaks into training, training accuracy is artificially high but test accuracy drops.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.