The answer is SageMaker distributed training with model parallelism. This feature resolves out-of-memory errors by splitting the model’s layers and parameters across multiple GPUs or instances, so that each device holds only a portion of the model, drastically reducing per-instance memory consumption without requiring a larger instance type. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to scale deep learning workloads when a model is too large for a single GPU’s memory—a common scenario with large transformers or computer vision models. A frequent trap is confusing model parallelism with data parallelism, which replicates the entire model on each instance and thus does not solve memory issues. Remember the mnemonic: “Model splits the memory load, data splits the data load.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company is training a deep learning model on Amazon SageMaker. The training job is failing with an out-of-memory error. Which SageMaker feature should the company use to resolve this issue without changing the instance type?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker distributed training with model parallelism
Option D is correct because SageMaker Managed Spot Training can reduce costs but does not fix memory issues. Option A and B are about debugging, not memory. Option C (distributed training) can split the model across instances, reducing per-instance memory usage. Option E is about cost, not memory.
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.
✓
Use SageMaker distributed training with model parallelism
Why this is correct
Model parallelism splits the model across multiple instances, reducing memory per instance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Savings Plans
Why it's wrong here
Savings Plans are for cost savings, not memory.
✗
Enable SageMaker Managed Spot Training
Why it's wrong here
Spot Training reduces cost but does not affect memory.
✗
Use SageMaker Debugger to monitor memory usage
Why it's wrong here
Debugger monitors but does not reduce memory usage.
✗
Enable SageMaker Profiler to profile memory
Why it's wrong here
Profiler analyzes but does not fix the issue.
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.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker distributed training with model parallelism — Option D is correct because SageMaker Managed Spot Training can reduce costs but does not fix memory issues. Option A and B are about debugging, not memory. Option C (distributed training) can split the model across instances, reducing per-instance memory usage. Option E is about cost, not memory.
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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