hardMultiple Choice
AIF-C01 Practice Question: A team is fine-tuning a Meta Llama 2 model on…
A team is fine-tuning a Meta Llama 2 model on Amazon Bedrock for a legal document classification task. After fine-tuning, the model performs well on the training set but poorly on the validation set. Which adjustment is MOST likely to reduce overfitting?
⚠ Common exam trap
AWS often tests the misconception that reducing epochs alone is a sufficient fix for overfitting, when in reality, regularization techniques like dropout and data augmentation are more targeted and effective for deep learning models.
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
✓
Increase the size of the training dataset and apply dropout
Increasing the training dataset size provides more diverse examples, helping the model generalize better, while dropout randomly deactivates neurons during training to prevent co-adaptation, both of which directly combat overfitting. In the context of fine-tuning Meta Llama 2 on Amazon Bedrock, these techniques are standard regularization methods to improve validation performance when the model memorizes the training set.
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 the size of the training dataset and apply dropout
Why this is correct
More data helps generalization; dropout randomly drops units during training, reducing overfitting.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate can cause divergence or poor convergence, not reduce overfitting.
- ✗
Add more layers to the model
Why it's wrong here
More layers increase model capacity, likely worsening overfitting.
- ✗
Reduce the number of training epochs
Why it's wrong here
Fewer epochs may underfit; overfitting is typically reduced by more data or regularization, not simply fewer epochs.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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