- A
Increase the instance type to ml.p3dn.24xlarge and use EFA networking.
Why wrong: This improves networking but does not address the I/O bottleneck from CSV format and default data loading.
- B
Tune hyperparameters using SageMaker Automatic Model Tuning to reduce training epochs.
Why wrong: Hyperparameter tuning may improve convergence but not necessarily address low GPU utilization due to I/O.
- C
Use SageMaker Debugger to profile the training and adjust the batch size to maximize GPU memory usage.
Why wrong: Debugger helps identify bottlenecks but alone does not change the underlying I/O inefficiency; adjusting batch size may not be enough.
- D
Convert the training data to Parquet format, use Pipe input mode in the training job, and increase the instance count to run distributed training.
Parquet reduces data size and improves I/O; Pipe mode streams data efficiently; distributed training scales out to reduce time.
Boosting GPU Utilization with Data Format and Distributed Training
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 financial services company is developing a real-time fraud detection model using XGBoost on SageMaker. They have millions of transactions daily and train a model weekly on 6 months of historical data. The training dataset is 500 GB in CSV format stored in S3. The training job uses an ml.p3.16xlarge instance with 8 GPUs, but training takes over 12 hours, which is too long for the weekly cadence. The data scientist notices that GPU utilization averages only 15% during training. The training script uses the SageMaker XGBoost container with default hyperparameters. Which combination of actions would MOST likely reduce training time? (Choose the best answer.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Convert the training data to Parquet format, use Pipe input mode in the training job, and increase the instance count to run distributed training.
Option D is correct because converting CSV to Parquet reduces data size and improves I/O efficiency, Pipe input mode streams data directly to the algorithm without downloading, and increasing instance count enables distributed training across multiple GPUs. These changes directly address the low GPU utilization (15%) by reducing data loading bottlenecks and parallelizing computation, which is the core issue with the current single-instance, CSV-based training.
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 instance type to ml.p3dn.24xlarge and use EFA networking.
Why it's wrong here
This improves networking but does not address the I/O bottleneck from CSV format and default data loading.
- ✗
Tune hyperparameters using SageMaker Automatic Model Tuning to reduce training epochs.
Why it's wrong here
Hyperparameter tuning may improve convergence but not necessarily address low GPU utilization due to I/O.
- ✗
Use SageMaker Debugger to profile the training and adjust the batch size to maximize GPU memory usage.
Why it's wrong here
Debugger helps identify bottlenecks but alone does not change the underlying I/O inefficiency; adjusting batch size may not be enough.
- ✓
Convert the training data to Parquet format, use Pipe input mode in the training job, and increase the instance count to run distributed training.
Why this is correct
Parquet reduces data size and improves I/O; Pipe mode streams data efficiently; distributed training scales out to reduce time.
Clue confirmation
The clue words "best", "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates focus on GPU hardware upgrades (Option A) or hyperparameter tuning (Option B) without recognizing that the root cause is data I/O inefficiency from CSV format and single-instance training, which is a classic SageMaker optimization scenario.
Detailed technical explanation
How to think about this question
Parquet format uses columnar storage and compression (e.g., Snappy), which can reduce data size by 70-80% compared to CSV, drastically cutting S3 read time. Pipe input mode streams data via Unix pipes, allowing the algorithm to start processing while data is still being read, eliminating the need to download and decompress the entire 500 GB dataset to local storage. Distributed training with SageMaker XGBoost uses the Rabit library for all-reduce communication, enabling multiple instances to split the data and train in parallel, which scales nearly linearly with instance count.
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 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: Convert the training data to Parquet format, use Pipe input mode in the training job, and increase the instance count to run distributed training. — Option D is correct because converting CSV to Parquet reduces data size and improves I/O efficiency, Pipe input mode streams data directly to the algorithm without downloading, and increasing instance count enables distributed training across multiple GPUs. These changes directly address the low GPU utilization (15%) by reducing data loading bottlenecks and parallelizing computation, which is the core issue with the current single-instance, CSV-based training.
What should I do if I get this MLA-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: "best", "most likely". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 →
Same concept, more angles
1 more ways this is tested on MLA-C01
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 company uses SageMaker to train a model with a large dataset stored in S3. They notice that the training job is taking longer than expected and the GPU utilization is low. Which action would most likely improve GPU utilization?
hard- ✓ A.Increase the batch size
- B.Disable distributed training
- C.Use a smaller instance type
- D.Decrease the batch size
Why A: Low GPU utilization during training often indicates that the GPU is waiting for data to process, a condition known as data bottleneck. Increasing the batch size allows each training step to process more samples, which increases the computational load per step and keeps the GPU busy for longer periods, thereby improving utilization. This is especially effective when using SageMaker's managed training with large datasets stored in S3, as larger batches reduce the frequency of data loading operations.
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Last reviewed: Jul 4, 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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