- A
Increase the number of data loading workers (num_workers).
Increasing num_workers parallelizes data loading, reducing CPU bottleneck and improving GPU utilization.
- B
Use a larger instance with more vCPUs.
Why wrong: More vCPUs alone do not help if the data loading is not parallelized; the main process remains the bottleneck.
- C
Increase the number of GPUs per instance.
Why wrong: Adding more GPUs does not solve the data loading bottleneck; it may increase contention for the same data loading thread.
- D
Switch from Pipe mode to File mode.
Why wrong: Switching to File mode would increase I/O overhead as it requires downloading files locally, likely reducing throughput further.
Question 96 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A team is using SageMaker to run a large-scale…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 team is using SageMaker to run a large-scale distributed training job for a language model. They are using SageMaker's Pipe mode to stream data from S3 to reduce IO. They observe that the training throughput is lower than expected, and the CPU utilization is high while GPU utilization is low. The training script uses PyTorch's DataLoader with num_workers=0. The data preprocessing is minimal. Which change is most likely to improve GPU utilization?
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 choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of data loading workers (num_workers).
With num_workers=0, PyTorch's DataLoader loads data in the main training process, creating a CPU bottleneck that keeps GPUs idle. Increasing num_workers parallelizes data loading across multiple subprocesses, which reduces CPU strain and feeds data faster to GPUs, improving throughput. Adding more GPUs (Option C) or vCPUs (Option B) does not address the root cause, and switching to File mode (Option D) would increase I/O overhead, worsening performance.
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.
- ✓
Increase the number of data loading workers (num_workers).
Why this is correct
Increasing num_workers parallelizes data loading, reducing CPU bottleneck and improving GPU utilization.
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 a larger instance with more vCPUs.
Why it's wrong here
More vCPUs alone do not help if the data loading is not parallelized; the main process remains the bottleneck.
- ✗
Increase the number of GPUs per instance.
Why it's wrong here
Adding more GPUs does not solve the data loading bottleneck; it may increase contention for the same data loading thread.
- ✗
Switch from Pipe mode to File mode.
Why it's wrong here
Switching to File mode would increase I/O overhead as it requires downloading files locally, likely reducing throughput further.
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Identify which MLA-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
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Increase the number of data loading workers (num_workers). — With num_workers=0, PyTorch's DataLoader loads data in the main training process, creating a CPU bottleneck that keeps GPUs idle. Increasing num_workers parallelizes data loading across multiple subprocesses, which reduces CPU strain and feeds data faster to GPUs, improving throughput. Adding more GPUs (Option C) or vCPUs (Option B) does not address the root cause, and switching to File mode (Option D) would increase I/O overhead, worsening performance.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 23, 2026
This MLA-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 MLA-C01 exam.
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