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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
The SageMaker HuggingFace estimator is specifically designed to simplify fine-tuning of pre-trained Hugging Face models like BERT. It automatically handles the underlying infrastructure, including the correct PyTorch/TensorFlow and Transformers versions, without requiring custom Docker containers. This is the recommended approach for Hugging Face model fine-tuning on SageMaker.
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 simplifies fine-tuning with pre-built containers.
- ✗
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
Versions ensure compatibility with the pre-trained model.
- ✗
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.