AI0-001 Machine Learning and Deep Learning Practice Question
A team is training a convolutional neural network (CNN) for medical image diagnosis. They have a limited dataset of 500 labeled images. Which strategy is most effective to improve model generalization?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that increasing model complexity (depth or filters) always improves performance, but with limited data, the correct strategy is to use regularization techniques like data augmentation to combat overfitting.
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
✓
Data augmentation
With only 500 labeled medical images, the primary challenge is overfitting due to limited data. Data augmentation (e.g., random rotations, flips, zooms) artificially expands the training set by creating varied but realistic transformations, which forces the CNN to learn invariant features and significantly improves generalization to unseen 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.
- ✗
Increasing network depth
Why it's wrong here
More layers increase model complexity, likely causing overfitting with small data.
- ✓
Data augmentation
Why this is correct
Augmentation (e.g., rotation, flip) generates more training examples, improving generalization.
- ✗
Using a larger batch size
Why it's wrong here
Larger batch sizes can lead to overfitting and do not address limited data.
- ✗
Reducing the number of filters
Why it's wrong here
Reducing filters may underfit and does not solve data scarcity.
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.