MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to build a text classification model using a pre-trained BERT model. The dataset contains 10,000 labeled documents. The model is overfitting: training accuracy is 99%, validation accuracy is 85%. Which TWO of the following are most likely to help reduce overfitting? (Choose TWO.)
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 dropout rate during fine-tuning
Increasing dropout during fine-tuning adds regularization. Decreasing the learning rate can help the model converge to a better solution and prevent overfitting to the training set. Increasing batch size can sometimes regularize but is not as effective as dropout. Adding more layers increases model capacity and overfitting. Using a larger pre-trained model also increases capacity.
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 more transformer layers to the model
Why it's wrong here
More layers increase model capacity and overfitting.
- ✓
Increase the dropout rate during fine-tuning
Why this is correct
Dropout is a regularization technique that randomly drops units, reducing overfitting.
- ✗
Increase the batch size
Why it's wrong here
Larger batch sizes can sometimes hurt generalization and are not a standard regularization for overfitting.
- ✗
Use a larger pre-trained BERT model
Why it's wrong here
Larger models have more capacity and are more prone to overfitting on small datasets.
- ✓
Decrease the learning rate
Why this is correct
A lower learning rate can help the model generalize better by making smaller updates.
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Senior Network & Security Engineer · founder of Courseiva
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