AIF-C01 Applications of Foundation Models Practice Question

A data scientist is fine-tuning a foundation model on a custom dataset using Amazon SageMaker. After training, the model shows high accuracy on training data but poor on validation. Which action should be taken?

⚠ Common exam trap

AWS often tests the misconception that overfitting is solved by increasing model complexity or data augmentation, but the correct approach is to reduce capacity or add 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

✓

Reduce training epochs or add regularization

The model is overfitting, as indicated by high training accuracy but poor validation performance. Reducing training epochs or adding regularization (e.g., L1/L2 weight decay) directly addresses overfitting by limiting the model's capacity to memorize noise. In Amazon SageMaker, this can be implemented via hyperparameter tuning or by modifying the training script to include regularization terms.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Add dropout layers
Why it's wrong here
Dropout is regularization but not commonly added to fine-tuned models.
✓
Reduce training epochs or add regularization
Why this is correct
Reducing epochs prevents overfitting; regularization also helps.
✗
Increase learning rate
Why it's wrong here
Increasing learning rate may cause divergence, not reduce overfitting.
✗
Use a different foundation model
Why it's wrong here
Switching models doesn't address overfitting on current dataset.

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AIF-C01 Applications of Foundation Models Practice Question

Why each option matters

Option-by-option breakdown

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