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 inflates validation performance, because information from the validation set contaminates training. Here validation collapses to 60%, the opposite symptom. Leakage would be the correct diagnosis when validation accuracy is suspiciously high, often exceeding training accuracy or failing to degrade on truly unseen data.
- ✓
Overfitting
Why this is correct
A large gap between 99% training accuracy and 60% validation accuracy means the model memorised training noise rather than generalising. Overfitting is the mechanism: high variance causes the model to fit patterns specific to the training set that do not hold on unseen validation data.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity concerns correlated input features destabilising coefficient estimates in linear or logistic regression; it does not create a large train-validation accuracy gap in a neural network. It would be the answer when regression coefficients flip sign or have inflated variance while overall predictive accuracy stays acceptable.
- ✗
Underfitting
Why it's wrong here
Underfitting produces low training and validation accuracy together, since the model has not learned the underlying pattern. A 99% versus 60% gap instead indicates the model memorised training data. Underfitting would be the answer when both scores are poor and additional model capacity or features are required.
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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.