AI0-001 AI Concepts and Foundations Practice Question
A team is training a neural network for image classification. They observe that training loss decreases steadily but validation loss starts increasing after 20 epochs. What is the most likely issue?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between overfitting and underfitting by showing a loss curve that decreases then increases, which candidates may misinterpret as a learning rate issue or vanishing gradient 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
✓
Overfitting
The training loss decreasing while validation loss increases after 20 epochs is the classic signature of overfitting. The model is memorizing the training data (including noise) 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.
- ✗
Underfitting
Why it's wrong here
Underfitting would show high training loss too.
- ✗
Vanishing gradients
Why it's wrong here
Vanishing gradients would prevent learning, not cause validation loss increase.
- ✗
Data leakage
Why it's wrong here
Data leakage would cause both losses to be low initially.
- ✓
Overfitting
Why this is correct
Correct; the model is fitting noise in training data.
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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.