AI0-001 Machine Learning and Deep Learning Practice Question
A machine learning engineer has a dataset of 100,000 records. She splits it into 70% training, 15% validation, and 15% test sets. After training, the model achieves 95% accuracy on training and 85% on validation. What does the accuracy difference most likely indicate?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and data split issues, trapping candidates who mistake a performance gap for an insufficient validation set rather than recognizing it as a model generalization problem.
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
✓
The model is overfitting
The 10% gap between training accuracy (95%) and validation accuracy (85%) is a classic sign of overfitting. The model has memorized patterns specific to the training set rather than learning generalizable features, causing it to perform worse on unseen validation data. In machine learning, a significant drop in performance from training to validation indicates poor generalization, which is the hallmark of overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The validation set is too small
Why it's wrong here
A 10-point train-validation gap signals overfitting, not validation-set size; 15,000 records is ample for reliable validation. Small validation sets cause noisy, high-variance estimates, which would show as erratic scores across runs, not a consistent training advantage.
- ✗
The model generalizes well
Why it's wrong here
A 10-point gap between training and validation accuracy signals overfitting, since the model has fitted training-specific noise that does not transfer. Generalising well would require the two accuracies to be close together. It is tempting because 85% validation accuracy is respectable in isolation, but the comparison against 95% training accuracy is what matters.
- ✓
The model is overfitting
Why this is correct
The model fits training data closely but generalises worse to unseen validation data, a 10-point gap indicating overfitting. It has learned noise and idiosyncrasies rather than the underlying pattern, so validation accuracy lags training accuracy.
- ✗
The test set should be larger
Why it's wrong here
Enlarging the test set does not address a 10-point training-to-validation gap, which indicates overfitting; the test set is untouched during training and cannot cause that divergence. It is tempting because small test sets do give noisy estimates, and enlarging one would be correct when evaluation variance, not overfitting, is the concern.
About these practice questions
One of 962 original AI0-001 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.