- A
SageMaker generic estimator with a custom container
Why wrong: A custom container would require more effort; the Hugging Face estimator is simpler and officially supported.
- B
SageMaker Hugging Face estimator
The Hugging Face estimator is specifically designed for Hugging Face models, managing the Transformers library and tokenizers.
- C
SageMaker PyTorch estimator
Why wrong: While possible, the Hugging Face estimator is tailored for Hugging Face models and simplifies the setup.
- D
SageMaker TensorFlow estimator
Why wrong: Similar to PyTorch, TensorFlow estimator can work but the Hugging Face estimator is more appropriate.
Question 662 of 1,000
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker generic estimator with a custom container
Why it's wrong here
A custom container would require more effort; the Hugging Face estimator is simpler and officially supported.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker PyTorch estimator
Why it's wrong here
While possible, the Hugging Face estimator is tailored for Hugging Face models and simplifies the setup.
- ✗
SageMaker TensorFlow estimator
Why it's wrong here
Similar to PyTorch, TensorFlow estimator can work but the Hugging Face estimator is more appropriate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Similar to PyTorch, TensorFlow estimator can work but the Hugging Face estimator is more appropriate.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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