AI0-001 AI Models and Data Engineering Practice Question
A model's training accuracy is 99% but validation accuracy drops to 60%. What is the most likely issue?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and data leakage by presenting a large accuracy gap, where candidates might mistakenly attribute the issue to data leakage instead of recognizing that leakage typically inflates both accuracies rather than creating a divergence.
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
A training accuracy of 99% with a validation accuracy of only 60% is a classic symptom of overfitting. The model has memorized the training data, including noise and outliers, rather than learning generalizable patterns, causing it to perform poorly on unseen validation 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.
- ✗
Data leakage
Why it's wrong here
Data leakage typically inflates validation accuracy, not reduce it.
- ✓
Overfitting
Why this is correct
Overfitting leads to high training accuracy but low validation accuracy.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity affects parameter estimates but is less likely to cause such a large accuracy gap.
- ✗
Underfitting
Why it's wrong here
Underfitting would result in poor performance on both training and validation sets.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.