The answer is to upgrade to a larger instance type with more GPUs, such as the ml.p3.8xlarge. This is correct because when GPU utilization is consistently at 95% while CPU and I/O remain low, the GPU is the clear bottleneck; adding more GPUs directly parallelizes the computation, reducing training time without altering the algorithm or model architecture. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to diagnose resource bottlenecks in SageMaker training jobs—a common trap is to assume I/O or data format changes will help, but Pipe mode is already optimal here. Remember the key insight: high GPU utilization signals a compute-bound problem, not an I/O one, so the fix is scaling GPU resources, not tweaking the data pipeline. Memory tip: “High GPU? Go big GPU.”
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 data scientist is using Amazon SageMaker to train a deep learning model using a built-in algorithm. The training job uses an ml.p3.2xlarge instance and takes 10 hours to complete. The scientist wants to reduce training time without changing the algorithm or model architecture. The instance's GPU utilization is consistently at 95%, but CPU utilization is only 20%. The data input pipeline uses SageMaker Pipe mode with the 'TrainingInputMode' set to 'Pipe'. The training dataset is 200 GB in CSV format stored in S3. Which approach is most likely to reduce training time?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a larger instance type with more GPUs, such as ml.p3.8xlarge
Option D is correct. Since GPU utilization is high (95%), the GPU is the bottleneck. Upgrading to a more powerful GPU instance (e.g., p3.8xlarge with 4 GPUs) can reduce training time by parallelizing computation. Option A is wrong because File mode may not help and could increase I/O overhead. Option B is wrong because Pipe mode is already being used. Option C is wrong because reducing batch size could underutilize GPU further.
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.
✗
Switch from Pipe mode to File mode to reduce I/O overhead
Why it's wrong here
File mode copies data to local storage, which may increase startup time and not help GPU bottleneck.
✗
Use Pipe mode with 'S3DataType' as 'AugmentedManifestFile'
Why it's wrong here
AugmentedManifestFile is for different data format, not for reducing training time.
✓
Use a larger instance type with more GPUs, such as ml.p3.8xlarge
Why this is correct
More GPUs can parallelize computation and reduce training time.
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.
✗
Reduce the batch size to improve GPU utilization
Why it's wrong here
Reducing batch size may decrease GPU utilization.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Use a larger instance type with more GPUs, such as ml.p3.8xlarge — Option D is correct. Since GPU utilization is high (95%), the GPU is the bottleneck. Upgrading to a more powerful GPU instance (e.g., p3.8xlarge with 4 GPUs) can reduce training time by parallelizing computation. Option A is wrong because File mode may not help and could increase I/O overhead. Option B is wrong because Pipe mode is already being used. Option C is wrong because reducing batch size could underutilize GPU further.
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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