AIF-C01 Fundamentals of AI and ML Practice Question
A company is training a deep learning model for image classification. Which THREE practices help reduce overfitting? (Choose three.)
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that increasing model complexity (depth) or tuning the learning rate can mitigate overfitting, when in fact these changes either exacerbate the problem or address unrelated training dynamics.
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 (also known as weight decay) adds a penalty proportional to the square of the weight magnitudes to the loss function. This discourages the model from learning overly complex patterns by forcing weights to stay small, which reduces overfitting by limiting the model's capacity to fit noise in the training 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.
- ✓
L2 regularization
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
- ✗
Increasing model depth
Why it's wrong here
Increasing depth adds capacity, likely increasing overfitting.
- ✗
Increasing learning rate
Why it's wrong here
Higher learning rate may lead to divergence but does not reduce overfitting.
- ✓
Dropout
Why this is correct
Dropout randomly deactivates neurons during training to prevent co-adaptation.
- ✓
Data augmentation
Why this is correct
Augmentation artificially increases data variety, reducing overfitting.
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