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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.

A
Switch from Pipe mode to File mode to reduce I/O overhead
Why wrong: File mode copies data to local storage, which may increase startup time and not help GPU bottleneck.
B
Use Pipe mode with 'S3DataType' as 'AugmentedManifestFile'
Why wrong: AugmentedManifestFile is for different data format, not for reducing training time.
C
Use a larger instance type with more GPUs, such as ml.p3.8xlarge
More GPUs can parallelize computation and reduce training time.
D
Reduce the batch size to improve GPU utilization
Why wrong: Reducing batch size may decrease GPU utilization.

Full breakdown with real-world context →

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

✓

Use a larger instance type with more GPUs, such as ml.p3.8xlarge

Option C is correct because GPU utilization is already at 95%, indicating the GPU is the bottleneck. Switching to a larger instance type like ml.p3.8xlarge provides four times the number of GPUs (4 vs. 1), allowing parallel processing of more data and directly reducing wall-clock training time without altering the algorithm or model architecture. The low CPU utilization (20%) confirms that the data pipeline is not a bottleneck, so I/O optimizations are unlikely to help.

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

The trap here is that candidates often assume low CPU utilization indicates an I/O bottleneck and choose to optimize the data pipeline (e.g., Pipe mode changes), when in fact the high GPU utilization reveals the true bottleneck is compute capacity, making a larger instance with more GPUs the correct solution.

Detailed technical explanation

How to think about this question

Under the hood, SageMaker Pipe mode streams data directly from S3 to the training algorithm via a FIFO pipe, bypassing disk writes and reducing I/O latency. However, when GPU utilization is saturated, the limiting factor is GPU compute capacity, not data ingestion. The ml.p3.2xlarge has a single NVIDIA V100 GPU, while the ml.p3.8xlarge has four V100 GPUs, enabling data parallelism where each GPU processes a portion of the mini-batch, effectively multiplying throughput. In practice, this approach works best when the model fits in GPU memory and the framework (e.g., TensorFlow, PyTorch) supports multi-GPU training, which built-in SageMaker algorithms typically do.

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.

TExam Day Tips

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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

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 C is correct because GPU utilization is already at 95%, indicating the GPU is the bottleneck. Switching to a larger instance type like ml.p3.8xlarge provides four times the number of GPUs (4 vs. 1), allowing parallel processing of more data and directly reducing wall-clock training time without altering the algorithm or model architecture. The low CPU utilization (20%) confirms that the data pipeline is not a bottleneck, so I/O optimizations are unlikely to help.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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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.