- A
Split the dataset into smaller files
Why wrong: Splitting files can help with I/O but is not a requirement for distributed training.
- B
Use SageMaker's distributed data parallelism library
Why wrong: While SageMaker offers a library, the more general step is to modify the script; this option is too specific and redundant with A.
- C
Modify the training script to use Horovod or PyTorch DistributedDataParallel
These frameworks enable multi-GPU communication and are necessary for distributed training.
- D
Enable automatic mixed precision
Why wrong: Mixed precision speeds up training on a single GPU but does not enable distributed training across instances.
- E
Increase the number of worker instances in the training job
Distributed training requires multiple instances to parallelize the workload.
Quick Answer
The correct two steps are to increase the number of worker instances in the training job and modify the training script to use a distributed framework like PyTorch DDP or Horovod. Distributed training on SageMaker requires both a hardware change—scaling out to multiple GPU instances—and a software change, because the training script must be rewritten to coordinate gradient updates across those workers. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that simply adding instances without adapting the script will not parallelize the workload; a common trap is selecting “split the dataset into smaller files,” which improves I/O but does not enable distribution. Another trap is choosing “automatic mixed precision,” which accelerates a single GPU but does not distribute training. Remember the two-part rule: more workers plus a distributed framework. Memory tip: “Scale out, then shout out”—add instances, then update your code to communicate.
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 machine learning engineer is training a neural network using Amazon SageMaker. The training job uses a single GPU instance. To improve training speed using distributed training, which two steps should they take? (Select TWO.)
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
Modify the training script to use Horovod or PyTorch DistributedDataParallel
Distributed training requires both modifying the training script to use a distributed framework (e.g., Horovod, PyTorch DDP) and increasing the number of instances. Splitting the dataset into smaller files can improve I/O but is not about distribution. SageMaker's distributed data parallelism library is one option, but modifying the script with a framework is the general step. Automatic mixed precision improves speed on a single GPU but does not enable distributed 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.
- ✗
Split the dataset into smaller files
Why it's wrong here
Splitting files can help with I/O but is not a requirement for distributed training.
- ✗
Use SageMaker's distributed data parallelism library
Why it's wrong here
While SageMaker offers a library, the more general step is to modify the script; this option is too specific and redundant with A.
- ✓
Modify the training script to use Horovod or PyTorch DistributedDataParallel
Why this is correct
These frameworks enable multi-GPU communication and are necessary for distributed training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable automatic mixed precision
Why it's wrong here
Mixed precision speeds up training on a single GPU but does not enable distributed training across instances.
- ✓
Increase the number of worker instances in the training job
Why this is correct
Distributed training requires multiple instances to parallelize the workload.
Related concept
Read the scenario before looking for a memorised answer.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: Modify the training script to use Horovod or PyTorch DistributedDataParallel — Distributed training requires both modifying the training script to use a distributed framework (e.g., Horovod, PyTorch DDP) and increasing the number of instances. Splitting the dataset into smaller files can improve I/O but is not about distribution. SageMaker's distributed data parallelism library is one option, but modifying the script with a framework is the general step. Automatic mixed precision improves speed on a single GPU but does not enable distributed training.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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