- A
Use distributed training with multiple GPUs
Correct. Distributed training with multiple GPUs (e.g., SageMaker data parallelism) splits the data or model across devices, reducing per-epoch time.
- B
Use a larger instance type with more vCPUs
Why wrong: Incorrect. Deep learning is GPU-bound; more vCPUs have minimal impact on training speed.
- C
Use SageMaker managed spot training
Why wrong: Incorrect. SageMaker managed spot training saves cost but can increase training time due to interruptions.
- D
Use a smaller batch size initially and increase gradually (warm-up)
Correct. A smaller initial batch size with gradual warm-up helps the model converge faster, reducing total training time.
- E
Increase the number of epochs
Why wrong: Incorrect. Increasing epochs increases training time; it does not speed up the process.
MLS-C01 Distributed Training 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. A key principle to apply: distributed Training. 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 company is training a deep learning model for object detection using Amazon SageMaker. The training job is taking too long. Which THREE actions can reduce training time?
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 distributed training with multiple GPUs
Distributed training (A) reduces wall-clock time by splitting the workload across multiple GPUs. Using a smaller batch size initially and increasing gradually (warm-up) (D) can help stabilize training and speed convergence by allowing the model to adjust more smoothly early on. Managed spot training (C) reduces cost, not training time, and may increase time due to interruptions. A larger instance (B) does not necessarily reduce training time if the bottleneck is GPU-related, and increasing the number of epochs (E) increases training time.
Key principle: Distributed Training
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 distributed training with multiple GPUs
Why this is correct
Correct. Distributed training with multiple GPUs (e.g., SageMaker data parallelism) splits the data or model across devices, reducing per-epoch time.
Related concept
Distributed Training
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
Incorrect. Deep learning is GPU-bound; more vCPUs have minimal impact on training speed.
- ✗
Use SageMaker managed spot training
Why it's wrong here
Incorrect. SageMaker managed spot training saves cost but can increase training time due to interruptions.
- ✓
Use a smaller batch size initially and increase gradually (warm-up)
Why this is correct
Correct. A smaller initial batch size with gradual warm-up helps the model converge faster, reducing total training time.
Related concept
Distributed Training
- ✗
Increase the number of epochs
Why it's wrong here
Incorrect. Increasing epochs increases training time; it does not speed up the process.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is confusing cost-saving techniques (like spot training) with performance-enhancing techniques (like distributed training).
Detailed technical explanation
How to think about this question
Under the hood, distributed training with multiple GPUs uses techniques like all-reduce (e.g., NCCL) to aggregate gradients across devices, achieving near-linear speedup if communication overhead is minimized. SageMaker's managed spot training (Option C) reduces cost but can also reduce time by allowing parallel use of cheaper, interruptible instances, though it may require checkpointing to handle interruptions. The warm-up batch size (Option D) helps stabilize training and can lead to faster convergence by allowing larger effective batch sizes later, reducing the number of steps needed.
KKey Concepts to Remember
- Distributed Training
- Batch Size Warm-up
- Managed Spot Training
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
Distributed Training
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.
Review distributed Training, then practise related MLS-C01 questions on the same topic to reinforce the concept.
- →
Modeling — study guide chapter
Learn the concepts, then practise the questions
- →
Modeling practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-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 MLS-C01 question test?
Modeling — This question tests Modeling — Distributed Training.
What is the correct answer to this question?
The correct answer is: Use distributed training with multiple GPUs — Distributed training (A) reduces wall-clock time by splitting the workload across multiple GPUs. Using a smaller batch size initially and increasing gradually (warm-up) (D) can help stabilize training and speed convergence by allowing the model to adjust more smoothly early on. Managed spot training (C) reduces cost, not training time, and may increase time due to interruptions. A larger instance (B) does not necessarily reduce training time if the bottleneck is GPU-related, and increasing the number of epochs (E) increases training time.
What should I do if I get this MLS-C01 question wrong?
Review distributed Training, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Distributed Training
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 MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
Last reviewed: Jun 30, 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.
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.