Quick Answer
The answer is to consolidate the small Parquet files into larger files, ideally around 1 GB each. This directly addresses the I/O bottleneck by reducing the overhead of opening and reading hundreds of thousands of small objects from S3, which starves the GPUs on the ml.p3.16xlarge instance. When training on large datasets, S3’s throughput is limited by the number of requests per second, not just bandwidth; larger files allow SageMaker to stream data more efficiently, keeping GPU utilization high. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of data loading patterns and the impact of file size on distributed training performance—a common trap is to assume Pipe mode or larger batch sizes alone will fix low GPU utilization, but they don’t solve the root cause of excessive S3 GET requests. Memory tip: think “big files, busy GPUs”—small files mean small throughput, so consolidate to accelerate.
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?
Increase the batch size
Why wrong: 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)
Larger files reduce I/O overhead.
Use a smaller instance type to reduce cost
Why wrong: Smaller instance would not improve speed.
Use Pipe input mode to stream data directly
Why wrong: Pipe mode can help with streaming but still benefits from larger files.
Answer choices
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 A is correct because consolidating small files into larger files reduces the overhead of reading many files from S3, improving I/O throughput and keeping GPUs busy. Option B (use Pipe mode) may help but does not address the file size issue. Option C (increase batch size) may improve utilization but the I/O bottleneck remains. Option D (use a smaller instance) would not improve speed.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Consolidate the small Parquet files into larger files (e.g., 1 GB each) — Option A is correct because consolidating small files into larger files reduces the overhead of reading many files from S3, improving I/O throughput and keeping GPUs busy. Option B (use Pipe mode) may help but does not address the file size issue. Option C (increase batch size) may improve utilization but the I/O bottleneck remains. Option D (use a smaller instance) would not improve speed.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to use Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is expected to take several hours. Which storage option should be used to minimize data loading time and cost?
easyWhy C: Option C is correct because Pipe mode streams data directly from S3 without downloading, minimizing time and cost. Option A (File mode) downloads entire dataset, increasing time and cost. Option B (Amazon EFS) is unnecessary and adds complexity. Option D (Amazon EBS) is not directly integrated with SageMaker.
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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