AI0-001 AI Concepts and Foundations Practice Question
Which TWO of the following are common techniques to reduce overfitting in a neural network?
⚠ Common exam trap
CompTIA often tests the misconception that adding more layers or training longer always improves accuracy, when in fact these actions typically increase overfitting without proper regularization or validation monitoring.
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
✓
L2 regularization
L2 regularization (option C) reduces overfitting by adding a penalty term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, preventing it from fitting noise in the training data and improving 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.
- ✗
Increasing the number of hidden layers
Why it's wrong here
Adding layers increases model capacity, likely increasing overfitting.
- ✗
Using a larger learning rate
Why it's wrong here
A larger learning rate may cause instability, but not specifically reduce overfitting.
- ✓
L2 regularization
Why this is correct
Correct; L2 regularization adds a penalty on squared weights.
- ✗
Training for more epochs
Why it's wrong here
More epochs can lead to overfitting if not monitored.
- ✓
Dropout
Why this is correct
Correct; dropout is a regularization technique.
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