MLS-C01 Modeling Practice Question
A company is fine-tuning a BERT model on Amazon SageMaker for a text classification task. The training script uses PyTorch and Hugging Face Transformers. The training job completes successfully, but the final model accuracy is low. The dataset has 10,000 labeled samples. What is the most likely cause and solution?
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
✓
The model is overfitting due to small dataset; use a pre-trained checkpoint and fine-tune only top layers
Fine-tuning the entire BERT model on only 10,000 samples leads to overfitting, resulting in low accuracy. The recommended approach is to use a pre-trained checkpoint and fine-tune only the top layers, which leverages transfer learning and reduces the risk of overfitting. Option A (instance type) impacts training speed, not accuracy directly. Option C (learning rate) could be a factor but overfitting is the most likely given the dataset size. Option D (data loader bug) would typically cause errors, not low accuracy without errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The instance type is insufficient; use a larger instance
Why it's wrong here
The instance type affects training speed, not accuracy. Insufficient instance type would cause out-of-memory or slow training, not necessarily low accuracy.
- ✓
The model is overfitting due to small dataset; use a pre-trained checkpoint and fine-tune only top layers
Why this is correct
Correct. Fine-tuning the entire BERT model on only 10,000 samples leads to overfitting. Using a pre-trained checkpoint and fine-tuning only top layers reduces overfitting and improves accuracy.
- ✗
The learning rate is too high; reduce it
Why it's wrong here
While a high learning rate can cause convergence issues, overfitting is the more likely cause given the small dataset size. Reducing learning rate might help but does not address overfitting directly.
- ✗
The training script has a bug in the data loader
Why it's wrong here
A bug in the data loader would typically cause runtime errors or incorrect data, not consistently low accuracy without any errors.
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