- A
Use SageMaker Pipe Mode to stream data directly from S3 to the algorithm, bypassing the local file system.
Why wrong: Pipe mode is designed for streaming, but it requires the algorithm to support it. The custom container may not, and even if it does, the single file still limits parallelism.
- B
Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3.
This allows SageMaker to parallelize data loading across multiple instances or even multiple processes within one instance, improving I/O throughput.
- C
Use Amazon SageMaker Managed Spot Training to reduce cost, then use the savings to rent a larger instance.
Why wrong: Spot instances reduce cost but not training time. The instance size has already been increased without improvement.
- D
Increase the number of training instances by using a distributed training configuration with Horovod.
Why wrong: With a single CSV file, adding more instances won't help because each instance still reads the same file, leading to contention and no speedup.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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.
An ML team is developing a regression model using Amazon SageMaker. They have a 100 GB CSV dataset stored in Amazon S3. The data is contained in a single large file. They launch a SageMaker training job with an ml.p3.8xlarge instance using a custom Docker container. The training script loads the data using pandas' read_csv from S3 directly. The team observes that the training job takes over 24 hours, and CloudWatch metrics show: GPU utilization is consistently above 90%, but CPU utilization is below 30%. Network I/O is moderate, and disk I/O is low. The team has already tried switching to a larger instance type (ml.p3.16xlarge) with no significant improvement. They need to reduce training time. Which action is MOST likely to achieve this?
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
Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3.
The bottleneck is data loading. The single large CSV file prevents parallelism; SageMaker's Pipe mode streams data directly to the algorithm, but custom containers must support it. However, a simpler and effective approach is to split the data into multiple smaller files, enabling SageMaker's distributed data loading across instances and improving I/O parallelism. Increasing instance count with single file doesn't help because each instance still reads the same file. Changing instance type already tried. Spot instances don't improve speed. EBS volume doesn't matter.
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.
- ✗
Use SageMaker Pipe Mode to stream data directly from S3 to the algorithm, bypassing the local file system.
Why it's wrong here
Pipe mode is designed for streaming, but it requires the algorithm to support it. The custom container may not, and even if it does, the single file still limits parallelism.
- ✓
Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3.
Why this is correct
This allows SageMaker to parallelize data loading across multiple instances or even multiple processes within one instance, improving I/O throughput.
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 Amazon SageMaker Managed Spot Training to reduce cost, then use the savings to rent a larger instance.
Why it's wrong here
Spot instances reduce cost but not training time. The instance size has already been increased without improvement.
- ✗
Increase the number of training instances by using a distributed training configuration with Horovod.
Why it's wrong here
With a single CSV file, adding more instances won't help because each instance still reads the same file, leading to contention and no speedup.
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 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?
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3. — The bottleneck is data loading. The single large CSV file prevents parallelism; SageMaker's Pipe mode streams data directly to the algorithm, but custom containers must support it. However, a simpler and effective approach is to split the data into multiple smaller files, enabling SageMaker's distributed data loading across instances and improving I/O parallelism. Increasing instance count with single file doesn't help because each instance still reads the same file. Changing instance type already tried. Spot instances don't improve speed. EBS volume doesn't matter.
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
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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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