AI Associate AI Fundamentals Practice Question
A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. 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: the model learns training data patterns too well, including noise, failing to generalize to new data.
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 it's wrong here
Concept drift affects performance over time, not a train-test split from the same time period.
- ✓
Overfitting
Why this is correct
Correct. Large gap between training and test performance indicates overfitting.
- ✗
Data leakage
Why it's wrong here
Data leakage would cause overly optimistic performance, but not necessarily a large gap.
- ✗
Underfitting
Why it's wrong here
Underfitting would show low accuracy on both training and test sets.
Go deeper
Related to this question
About these practice questions
Courseiva writes every AI Associate question from scratch — 753 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.