AIF-C01 Fundamentals of AI and ML Practice Question
A team is training a deep learning model using Horovod distributed training on SageMaker. They observe that the loss stops decreasing after a few epochs. Which technique should they implement to reduce overfitting?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that early stopping (reducing epochs) is a regularization technique to reduce overfitting, but the trap here is that early stopping only halts training and does not actively prevent the model from memorizing noise during the epochs it does train.
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
✓
Use dropout regularization
Dropout regularization randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and forces it to learn more robust representations. This directly addresses overfitting, which is the likely cause of the loss plateauing after a few epochs in a Horovod distributed training setup on SageMaker.
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 can cause the loss to diverge.
- ✗
Add more layers to the model
Why it's wrong here
More layers increase capacity and may exacerbate overfitting.
- ✗
Reduce the number of epochs
Why it's wrong here
Reducing epochs may underfit but does not directly address overfitting.
- ✓
Use dropout regularization
Why this is correct
Dropout is a regularization technique that reduces overfitting.
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