AI0-001 Machine Learning and Deep Learning Practice Question
A team is designing a deep learning pipeline for a computer vision task. They want to reduce overfitting. Which two techniques are specifically effective for this purpose? (Select TWO.)
⚠ Common exam trap
This exam often tests the misconception that increasing model capacity (more layers) or adjusting batch size directly reduces overfitting, when in fact these changes typically require additional regularization to be effective.
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
✓
Dropout
Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Dropout
Why this is correct
Dropout randomly deactivates neurons, reducing overfitting by preventing reliance on specific features.
- ✗
Using a smaller batch size
Why it's wrong here
Smaller batch size adds noise but is not a primary regularization technique; dropout and L2 are more effective.
- ✗
Adding more layers
Why it's wrong here
Adding layers increases model capacity, typically worsening overfitting.
- ✓
L2 weight regularization
Why this is correct
L2 regularization penalizes large weights, simplifying the model and reducing overfitting.
- ✗
Increasing the learning rate
Why it's wrong here
Increasing learning rate can cause divergence or unstable training, not reduce overfitting.
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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.