- 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.
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.
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.
The low GPU utilization suggests I/O bottleneck (data loading) or inefficient data format. Converting CSV to Parquet reduces data size and speeds up I/O. Using Pipe mode streamlines data loading from S3. Increasing instance type would further help if I/O is resolved. Option C directly addresses the root cause. Option A might not help if GPU is underutilized. Option B focuses on hyperparameters, which might not be the primary bottleneck. Option D spreads data but doesn't fix I/O if still CSV.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
- →
ML Model Development — study guide chapter
Learn the concepts, then practise the questions
- →
ML Model Development practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
507 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Model Development — This question tests ML Model Development — Static NAT maps one inside address to one outside address..
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. — The low GPU utilization suggests I/O bottleneck (data loading) or inefficient data format. Converting CSV to Parquet reduces data size and speeds up I/O. Using Pipe mode streamlines data loading from S3. Increasing instance type would further help if I/O is resolved. Option C directly addresses the root cause. Option A might not help if GPU is underutilized. Option B focuses on hyperparameters, which might not be the primary bottleneck. Option D spreads data but doesn't fix I/O if still CSV.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
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?
Static NAT maps one inside address to one outside address.
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 →
Keep practising
More MLA-C01 practice questions
- A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality…
- A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accura…
- A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices shou…
- A company is using SageMaker to train a neural network for image classification. The training job is taking too long. Th…
- A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target vari…
- A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multipl…
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.