The answer is to enable SageMaker's distributed data parallelism. This is correct because data parallelism splits the large training dataset across multiple instances, so each instance processes only a fraction of the data at a time, directly reducing per-instance memory usage without altering the fixed model architecture or hyperparameters. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s built-in distributed training strategies versus simple instance scaling—a common trap is assuming that increasing instance memory (like moving to a larger ml.p3 instance) will always fix memory issues, but if the training script itself is memory-inefficient, a larger instance may still fail. Another trap is confusing Pipe mode (which reduces local disk storage) with memory management. For the exam, remember the mnemonic: “Data splits, memory fits”—when memory errors occur during SageMaker training and the model code is fixed, think distributed data parallelism first.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 using a large dataset stored in S3. The training job is failing with an error indicating insufficient memory. The model architecture and hyperparameters are fixed. Which change is MOST likely to resolve the issue without modifying the model code?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Distributed data parallelism splits the minibatch across multiple GPUs/instances, reducing per-device memory footprint.
B
Use managed Spot training to get cheaper compute.
Why wrong: Spot instances provide lower cost but do not change the memory available to the training job.
C
Use a larger instance type with more memory.
Why wrong: While this may work, it does not address the root cause if the model requires more memory than available; also may increase cost unnecessarily.
D
Use Pipe mode for input data instead of File mode.
Why wrong: Pipe mode streams data directly to the algorithm, reducing local disk usage, but does not reduce memory consumption of the model.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable SageMaker's distributed data parallelism.
Option C is correct because enabling data parallelism with SageMaker distributed training splits the data across multiple instances, reducing per-instance memory usage. Option A is wrong because increasing instance memory does not address root cause if training script uses memory inefficiently. Option B is wrong because using Pipe mode reduces disk usage but not memory. Option D is wrong because Spot instances do not affect 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.
✓
Enable SageMaker's distributed data parallelism.
Why this is correct
Distributed data parallelism splits the minibatch across multiple GPUs/instances, reducing per-device memory footprint.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use managed Spot training to get cheaper compute.
Why it's wrong here
Spot instances provide lower cost but do not change the memory available to the training job.
✗
Use a larger instance type with more memory.
Why it's wrong here
While this may work, it does not address the root cause if the model requires more memory than available; also may increase cost unnecessarily.
✗
Use Pipe mode for input data instead of File mode.
Why it's wrong here
Pipe mode streams data directly to the algorithm, reducing local disk usage, but does not reduce memory consumption of the model.
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: Enable SageMaker's distributed data parallelism. — Option C is correct because enabling data parallelism with SageMaker distributed training splits the data across multiple instances, reducing per-instance memory usage. Option A is wrong because increasing instance memory does not address root cause if training script uses memory inefficiently. Option B is wrong because using Pipe mode reduces disk usage but not memory. Option D is wrong because Spot instances do not affect 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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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