The answer is SageMaker’s model parallelism with automatic partitioning. This is the correct choice because when training a large language model with 1 billion parameters on a cluster of 16 p4d.24xlarge instances, the model simply does not fit on a single GPU, and SageMaker’s model parallelism automatically splits the model layers and tensors across multiple GPUs and instances, requiring minimal script changes compared to manual sharding or data parallelism alone. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of when to use model parallelism versus data parallelism—a common trap is assuming data parallelism suffices for any large model, but it only works when the entire model fits on one device. Remember the memory tip: “If it doesn’t fit on one GPU, think model parallelism; if it fits but you need speed, think data parallelism.” This distinction is key for training large models efficiently with SageMaker.
MLS-C01 Modeling Practice Question
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 research lab is training a large language model (LLM) on SageMaker using PyTorch. The model has 1 billion parameters and does not fit on a single GPU. They have access to a cluster of 16 p4d.24xlarge instances (each with 8 A100 GPUs). They need to train the model with minimal changes to the training script. Which SageMaker feature should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker's model parallelism with automatic partitioning
SageMaker's model parallelism is designed for large models that don't fit on a single device.
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's model parallelism with automatic partitioning
Why this is correct
Model parallelism splits the model across GPUs, and SageMaker's library automates this.
Related concept
Read the scenario before looking for a memorised answer.
✗
SageMaker's distributed data parallelism with Horovod
Why it's wrong here
Data parallelism requires the model to fit on one GPU; LLMs often need model parallelism.
✗
Use SageMaker's built-in BlazingText algorithm
Why it's wrong here
BlazingText is for word embeddings, not LLMs.
✗
SageMaker's managed spot training with checkpointing
Why it's wrong here
Spot training addresses cost, not model size.
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.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SageMaker's model parallelism with automatic partitioning — SageMaker's model parallelism is designed for large models that don't fit on a single device.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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