AI0-001 AI Concepts and Foundations Practice Question
Which TWO of the following are techniques used for reducing overfitting in neural networks? (Choose two.)
⚠ Common exam trap
CompTIA often tests the distinction between regularization techniques and other training strategies, so the trap here is that candidates may confuse boosting (an ensemble method) with regularization, or assume that increasing model complexity (more layers) or learning rate can help reduce overfitting when they actually do the opposite.
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 drops neurons to reduce overfitting.
- ✗
Boosting
Why it's wrong here
Boosting combines weak learners and may overfit if not carefully tuned.
- ✓
L2 regularization
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
- ✗
Increasing the learning rate
Why it's wrong here
Learning rate affects convergence, not overfitting directly.
- ✗
Increasing the number of hidden layers
Why it's wrong here
More layers increase model complexity, potentially worsening overfitting.
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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.