AI Associate AI Fundamentals Practice Question
A data scientist trains a lead scoring model that achieves 99% accuracy on training data but only 65% accuracy on a held-out test set. What is the most likely issue?
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 due to model complexity or insufficient regularization
Overfitting occurs when the model memorizes training data noise instead of learning generalizable patterns, leading to poor 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.
- ✗
Underfitting due to insufficient model complexity
Why it's wrong here
Underfitting would show poor performance on both training and test sets, not high training accuracy.
- ✓
Overfitting due to model complexity or insufficient regularization
Why this is correct
Overfitting explains the large gap between high training accuracy and low test accuracy.
- ✗
Label noise in the training data
Why it's wrong here
Label noise usually degrades training accuracy as well, not causing such a stark contrast.
- ✗
Data leakage from the test set into training
Why it's wrong here
Data leakage could cause inflated performance, but typically affects both sets similarly; here training far exceeds test.
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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.