MLA-C01 ML Model Development Practice Question
A team wants to fine-tune a pre-trained Hugging Face transformer model for text classification using SageMaker. They have a custom training script. Which SageMaker estimator should they use?
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
✓
SageMaker Hugging Face estimator
The Hugging Face estimator is the recommended way to run Hugging Face models on SageMaker, as it automatically handles the environment and dependencies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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SageMaker generic estimator with a custom container
Why it's wrong here
Using a generic estimator with a custom container requires you to build and host your own Docker image, which is unnecessary here because SageMaker’s built-in Hugging Face estimator directly supports the pre-trained transformer model and custom training script without container management. This option tempts because a generic estimator is the correct choice when you need to bring a completely custom framework or library not natively supported by SageMaker.
- ✓
SageMaker Hugging Face estimator
Why this is correct
The Hugging Face estimator is specifically designed for Hugging Face models, managing the Transformers library and tokenizers.
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SageMaker PyTorch estimator
Why it's wrong here
While possible, the Hugging Face estimator is tailored for Hugging Face models and simplifies the setup.
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SageMaker TensorFlow estimator
Why it's wrong here
The SageMaker TensorFlow estimator is incorrect because the scenario specifies a Hugging Face transformer model, which primarily uses PyTorch. This estimator is tailored for TensorFlow-based training scripts and environments, lacking native support for PyTorch dependencies without manual configuration. It is tempting because TensorFlow is a widely used deep learning framework. This estimator would be the correct choice if the custom training script and the pre-trained model were explicitly built using TensorFlow.
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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.