MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to build a text classification model. The dataset has 100,000 labeled samples and 20 classes. The scientist wants to use a pre-trained BERT model and fine-tune it. Which approach is MOST cost-effective?
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
✓
Fine-tune a pre-trained BERT-base model using a GPU instance.
Fine-tuning a pre-trained BERT-base model on a GPU instance is the most cost-effective approach. Pre-trained BERT models already capture general language features, so fine-tuning requires less computation and data compared to training from scratch. BERT-base is smaller than BERT-large, reducing cost while still being effective for text classification. Option A is wrong because training BERT from scratch is extremely expensive and unnecessary. Option C is wrong because BERT-large has higher cost and may overfit given the dataset size. Option D is wrong because CNN models from scratch would require more hyperparameter tuning and may not achieve comparable accuracy to a fine-tuned transformer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train a BERT model from scratch using a larger instance.
Why it's wrong here
Training from scratch is expensive and unnecessary.
- ✓
Fine-tune a pre-trained BERT-base model using a GPU instance.
Why this is correct
BERT-base is cost-effective and fine-tuning is efficient.
- ✗
Use a pre-trained BERT-large model with a larger instance.
Why it's wrong here
BERT-large is more expensive and may not provide significant gain.
- ✗
Train a CNN model from scratch using CPU instances.
Why it's wrong here
Training CNN from scratch is less effective for text.
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