AI0-001 AI Concepts and Foundations Practice Question
A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?
⚠ Common exam trap
CompTIA often tests the misconception that plateauing training loss always requires adjusting learning rate or batch size, when in fact the simultaneous rise in validation loss is the definitive indicator of overfitting that early stopping is designed to solve.
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 based on validation loss to prevent further overfitting.
The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the batch size to introduce more noise during training.
Why it's wrong here
Smaller batch size may help generalization but is not the most direct fix for overfitting; early stopping is standard.
- ✗
Increase the learning rate to help the model escape the plateau.
Why it's wrong here
Increasing learning rate may cause divergence; validation loss increase suggests overfitting, not plateau.
- ✓
Implement early stopping based on validation loss to prevent further overfitting.
Why this is correct
Early stopping stops training before overfitting worsens.
- ✗
Add more convolutional layers to increase model capacity.
Why it's wrong here
More capacity often leads to more overfitting without regularization.
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.