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MLA-C01 Practice Question: A data scientist is building a text…
A data scientist is building a text classification model using a pre-trained BERT model from the Hugging Face library on SageMaker. The scientist wants to fine-tune the model on a custom dataset. Which TWO steps are necessary to set up the fine-tuning job? (Select TWO.)
⚠ Common exam trap
AWS often tests the misconception that custom Docker containers are required for any non-standard framework, but the HuggingFace estimator eliminates that need by providing a managed environment with version control.
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
✓
Use the HuggingFace estimator provided by SageMaker
Option A is correct because the HuggingFace estimator from the SageMaker Python SDK is the purpose-built, supported interface for launching Hugging Face training jobs on SageMaker; it handles the training container, script entry point, and hyperparameters needed to fine-tune a pre-trained BERT model. Option D is correct because the HuggingFace estimator lets you pin the exact framework and library versions via the transformers_version and pytorch_version (or tensorflow_version) arguments, which is essential for reproducibility and compatibility with the pre-trained BERT checkpoint. Option B is not needed because SageMaker Clarify provides bias and explainability analysis, not a prerequisite for fine-tuning. Option C is unnecessary since the HuggingFace estimator already supplies a managed container with PyTorch and Transformers, so building a custom Docker image is only required for unsupported dependencies. Option E is not required because data preprocessing can be done inside the training script or beforehand; SageMaker Processing is optional and not a mandatory setup step for the fine-tuning job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the HuggingFace estimator provided by SageMaker
Why this is correct
The HuggingFace estimator is the SageMaker-provided class that packages the training container, script and hyperparameters for Hugging Face models. It satisfies the requirement to set up a fine-tuning job for the pre-trained BERT model on a custom dataset.
- ✗
Enable SageMaker Clarify for explainability during training
Why it's wrong here
Clarify is for bias detection and explainability, not for training setup.
- ✗
Build a custom Docker container with PyTorch and Transformers
Why it's wrong here
SageMaker provides built-in HuggingFace containers, so custom container is not needed.
- ✓
Specify the PyTorch framework version and Transformers version in the estimator
Why this is correct
SageMaker's Hugging Face estimator requires explicit framework_version and transformers_version arguments so the training container matches the BERT implementation; without pinning both, the job may pull an incompatible image and fail to load the pre-trained weights.
- ✗
Use SageMaker Processing to preprocess the data in parallel
Why it's wrong here
Processing is for data preprocessing, not for fine-tuning setup.
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