AIF-C01 Fundamentals of AI and ML Practice Question
During model training, the loss decreases rapidly for the first few epochs and then plateaus. The validation loss starts increasing after some epochs. What should the team do to improve generalization?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that overfitting is solved by increasing model complexity or training longer, when in fact the opposite is true—early stopping or regularization techniques are required to curb overfitting.
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
✓
Early stopping
The validation loss increasing while training loss continues to decrease is a classic sign of overfitting. Early stopping (Option B) halts training when validation performance stops improving, preventing the model from memorizing noise in the training data and thereby 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.
- ✗
Increase learning rate
Why it's wrong here
Increasing learning rate may cause instability and not address overfitting.
- ✓
Early stopping
Why this is correct
Early stopping stops training before overfitting occurs.
- ✗
Increase training epochs
Why it's wrong here
More epochs would likely increase overfitting.
- ✗
Add more layers
Why it's wrong here
Adding layers increases model capacity and can worsen overfitting.
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