AI0-001 AI Concepts and Foundations Practice Question
An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?
⚠ Common exam trap
CompTIA often tests the distinction between techniques that address overfitting (like dropout) versus those that improve convergence (like learning rate adjustment) or model capacity (like adding layers), trapping candidates who confuse regularization with optimization.
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
✓
Add dropout layers
The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase data augmentation
Why it's wrong here
Augmentation helps but dropout is more targeted for overfitting.
- ✗
Decrease the learning rate
Why it's wrong here
Learning rate affects convergence, not generalization.
- ✗
Increase the number of layers
Why it's wrong here
More layers increase capacity, likely worsening overfitting.
- ✓
Add dropout layers
Why this is correct
Dropout reduces overfitting by preventing co-adaptation of neurons.
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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.