Quick Answer
The answer is the SageMaker model parallelism library (SMP). This is the correct choice because when training a large language model that requires significant memory, standard data parallelism—where each GPU holds a full copy of the model—quickly runs out of memory. Model parallelism, as implemented by SMP, shards the model’s layers and parameters across multiple GPUs, allowing you to train architectures that would otherwise be too large for a single device. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between distributed training strategies: Horovod is a common trap for data parallelism, while SMP is the specific SageMaker feature for model parallelism. Remember that SMP is purpose-built for large models like LLMs, not for smaller models that fit in GPU memory. A helpful memory tip: think “SMP = Split My Parameters” across GPUs to handle large language models.
This MLS-C01 practice question tests your understanding of modeling. 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 machine learning team is using Amazon SageMaker to train a large language model. The training script uses PyTorch and the model requires significant memory. The team wants to use model parallelism across multiple GPUs. Which SageMaker feature should they use?
SageMaker Distributed Training
Why wrong: While correct in concept, the specific feature is the model parallelism library.
SageMaker model parallelism library
SMP is specifically designed for model parallelism.
SageMaker Horovod
Why wrong: Horovod supports data parallelism, not model parallelism.
SageMaker Debugger
Why wrong: Debugger monitors training metrics, not for parallelism.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SageMaker model parallelism library
SageMaker's model parallelism library (SMP) is designed for distributed training of large models across GPUs. Horovod is for data parallelism, not model parallelism. SageMaker Debugger is for monitoring training. Distributed Training is a generic term; the specific library is SMP.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
SageMaker Distributed Training
Why it's wrong here
While correct in concept, the specific feature is the model parallelism library.
SageMaker model parallelism library
Why this is correct
SMP is specifically designed for model parallelism.
Related concept
Read the scenario before looking for a memorised answer.
SageMaker Horovod
Why it's wrong here
Horovod supports data parallelism, not model parallelism.
SageMaker Debugger
Why it's wrong here
Debugger monitors training metrics, not for parallelism.
Common exam traps
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.
Detailed technical explanation
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLS-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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: SageMaker model parallelism library — SageMaker's model parallelism library (SMP) is designed for distributed training of large models across GPUs. Horovod is for data parallelism, not model parallelism. SageMaker Debugger is for monitoring training. Distributed Training is a generic term; the specific library is SMP.
Identify which MLS-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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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