MLS-C01 Modeling Practice Question
Which THREE techniques can help reduce overfitting in a neural network? (Select THREE.)
⚠ Common exam trap
AWS often tests the misconception that adding more capacity (layers/epochs) always improves performance, when in fact it increases overfitting without proper regularization.
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 correct because it randomly deactivates a fraction of neurons during training, forcing the network to learn redundant representations and preventing co-adaptation of features. This reduces overfitting by acting as an ensemble method without increasing computational cost at inference time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase training epochs
Why it's wrong here
More epochs may overfit.
- ✓
Dropout
Why this is correct
Dropout randomly drops units.
- ✓
Early stopping
Why this is correct
Early stopping prevents overtraining.
- ✗
Increase the number of layers
Why it's wrong here
More layers increase complexity.
- ✓
L2 regularization
Why this is correct
L2 penalizes large weights.
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