AI0-001 Machine Learning and Deep Learning Practice Question
Which THREE techniques can help reduce overfitting in neural networks?
⚠ Common exam trap
CompTIA often tests the misconception that increasing model complexity (e.g., more layers or larger learning rates) can help with overfitting, when in fact these changes typically worsen it by increasing variance or destabilizing training.
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
✓
Increasing training data size
Increasing the training data size helps reduce overfitting by providing the model with more examples to learn from, which reduces the variance and improves generalization. With more data, the model is less likely to memorize noise and instead learns the underlying patterns, making it more robust on 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 training data size
Why this is correct
More data helps the model generalize better.
- ✓
L2 regularization
Why this is correct
L2 adds penalty on weights, discouraging overly complex models.
- ✗
Using a larger learning rate
Why it's wrong here
Larger learning rate can cause divergence; not a regularization technique.
- ✓
Dropout
Why this is correct
Dropout is a regularization technique that prevents co-adaptation.
- ✗
Increasing number of layers
Why it's wrong here
More layers increase model capacity, worsening overfitting.
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