- A
Data parallelism using SageMaker's Distributed Data Parallel
Data parallelism distributes the training across multiple GPUs, reducing training time for models that fit on a single GPU.
- B
Use a larger instance with more vCPUs
Why wrong: More vCPUs does not directly provide additional GPUs for data parallelism.
- C
Model parallelism using SageMaker's Model Parallel
Why wrong: Model parallelism is intended for models that cannot fit on a single GPU.
- D
Use Elastic Inference
Why wrong: Elastic Inference is for inference acceleration, not training.
Question 685 of 1,000
mediumMultiple ChoiceObjective-mapped
Data Parallelism vs Model Parallelism on SageMaker
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 training a large model on SageMaker and wants to reduce training time by using multiple GPUs. The model is small enough to fit on a single GPU but training is slow. Which SageMaker feature should be used?
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
✓
Data parallelism using SageMaker's Distributed Data Parallel
SageMaker's Distributed Data Parallel (DDP) is the correct choice because it splits the mini-batch across multiple GPUs, allowing each GPU to hold a copy of the model and process a subset of the data simultaneously. This reduces training time for models that fit on a single GPU by leveraging data parallelism, where gradients are synchronized across GPUs after each step.
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.
- ✓
Data parallelism using SageMaker's Distributed Data Parallel
Why this is correct
Data parallelism distributes the training across multiple GPUs, reducing training time for models that fit on a single GPU.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger instance with more vCPUs
Why it's wrong here
More vCPUs does not directly provide additional GPUs for data parallelism.
- ✗
Model parallelism using SageMaker's Model Parallel
Why it's wrong here
Model parallelism is intended for models that cannot fit on a single GPU.
- ✗
Use Elastic Inference
Why it's wrong here
Elastic Inference is for inference acceleration, not training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse model parallelism (for large models) with data parallelism (for slow training of small models), or mistakenly think Elastic Inference can accelerate training when it is strictly for inference latency reduction.
Detailed technical explanation
How to think about this question
SageMaker's DDP uses NVIDIA's NCCL for all-reduce gradient synchronization, which is highly optimized for multi-GPU communication over NVLink or InfiniBand. In practice, data parallelism scales nearly linearly with the number of GPUs for models that fit in GPU memory, but only if the batch size is large enough to keep all GPUs busy and the gradient synchronization overhead is minimized. A common pitfall is using too small a global batch size, which can degrade model accuracy due to increased gradient noise.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Data parallelism using SageMaker's Distributed Data Parallel — SageMaker's Distributed Data Parallel (DDP) is the correct choice because it splits the mini-batch across multiple GPUs, allowing each GPU to hold a copy of the model and process a subset of the data simultaneously. This reduces training time for models that fit on a single GPU by leveraging data parallelism, where gradients are synchronized across GPUs after each step.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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