- A
Increase the batch size
Why wrong: Larger batch may increase GPU utilization but does not fix I/O bottleneck.
- B
Consolidate the small Parquet files into larger files (e.g., 1 GB each)
Larger files reduce I/O overhead.
- C
Use a smaller instance type to reduce cost
Why wrong: Smaller instance would not improve speed.
- D
Use Pipe input mode to stream data directly
Why wrong: Pipe mode can help with streaming but still benefits from larger files.
Question 1,707 of 1,755
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 data scientist is using Amazon SageMaker to train a model on a large dataset (10 TB) stored in S3 in Parquet format. The training job uses an ml.p3.16xlarge instance with multiple GPUs. The data scientist notices that the GPU utilization is low (around 30%) and the training is slow. The dataset consists of hundreds of thousands of small Parquet files. The data scientist suspects that the I/O is bottlenecked. What should the data scientist do to improve GPU utilization and training speed?
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
✓
Consolidate the small Parquet files into larger files (e.g., 1 GB each)
Option B is correct because consolidating small Parquet files into larger files (e.g., 1 GB each) reduces the overhead of reading many small files from S3, improving I/O throughput and keeping GPUs busy. Option A (increase batch size) may help GPU utilization but does not address the I/O bottleneck. Option C (use a smaller instance) would not improve speed and may worsen the situation. Option D (Pipe input mode) can help with streaming but does not solve the small file issue; the data still comes from many small files.
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 batch size
Why it's wrong here
Larger batch may increase GPU utilization but does not fix I/O bottleneck.
- ✓
Consolidate the small Parquet files into larger files (e.g., 1 GB each)
Why this is correct
Larger files reduce I/O overhead.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a smaller instance type to reduce cost
Why it's wrong here
Smaller instance would not improve speed.
- ✗
Use Pipe input mode to stream data directly
Why it's wrong here
Pipe mode can help with streaming but still benefits from larger files.
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.
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 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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Consolidate the small Parquet files into larger files (e.g., 1 GB each) — Option B is correct because consolidating small Parquet files into larger files (e.g., 1 GB each) reduces the overhead of reading many small files from S3, improving I/O throughput and keeping GPUs busy. Option A (increase batch size) may help GPU utilization but does not address the I/O bottleneck. Option C (use a smaller instance) would not improve speed and may worsen the situation. Option D (Pipe input mode) can help with streaming but does not solve the small file issue; the data still comes from many small files.
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.
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 20, 2026
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.
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