AI0-001 AI Concepts and Foundations Practice Question
Which three techniques are commonly used to mitigate overfitting in neural networks? (Choose three.)
⚠ Common exam trap
CompTIA often tests the distinction between data-level strategies (like increasing training data) and algorithmic regularization techniques (like L2, dropout, early stopping), leading candidates to mistakenly select 'increasing training data' as a technique when the question specifically asks for techniques commonly used within the neural network training process.
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
✓
Adding L2 regularization
Adding L2 regularization (also known as weight decay) penalizes large weights by adding a term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, reducing the model's sensitivity to noise in the training data and preventing it from fitting spurious patterns, which is a direct and effective method to combat 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.
- ✓
Adding L2 regularization
Why this is correct
L2 regularization adds a penalty on large weights, discouraging overfitting by constraining the model complexity.
- ✗
Increasing training data
Why it's wrong here
While more data can help, it is not a 'technique' in the same sense; it is a data collection strategy, not a regularization technique applied during training.
- ✓
Dropout
Why this is correct
Dropout randomly drops neurons during training, forcing the network to learn redundant representations.
- ✗
Reducing number of layers
Why it's wrong here
Reducing layers can reduce overfitting but also reduces model capacity significantly and may lead to underfitting; it is not a primary regularization technique.
- ✓
Early stopping
Why this is correct
Early stopping halts training when validation performance stops improving, preventing overfitting.
About these practice questions
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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.