AI0-001 AI Concepts and Techniques Practice Question
A team is training a deep learning model for image classification. The training loss decreases steadily but the validation loss plateaus after 20 epochs and then starts to increase. Which action is MOST likely to improve generalization?
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
✓
Implement early stopping
Early stopping halts training when validation loss stops improving, preventing overfitting. Increasing learning rate would worsen divergence; adding more layers increases capacity and overfitting; reducing batch size may help optimization but not directly address 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.
- ✗
Add more convolutional layers
Why it's wrong here
Adding layers increases model capacity, likely exacerbating overfitting.
- ✗
Increase the learning rate
Why it's wrong here
Increasing learning rate can cause the optimizer to overshoot minima and worsen validation loss.
- ✓
Implement early stopping
Why this is correct
Early stopping monitors validation loss and stops training before overfitting occurs, directly addressing the plateau and rise.
- ✗
Reduce the batch size
Why it's wrong here
Smaller batch sizes introduce noise that can help generalization but are less targeted than early stopping for this clear overfitting pattern.
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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.