This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is using SageMaker to train a deep learning model for image classification. The training job is taking too long. Which approach can reduce training time?
Use SageMaker's distributed data parallelism
Distributed training speeds up training by parallelizing across GPUs.
Use SageMaker Neo to compile the model
Why wrong: Neo is for inference optimization, not training.
Increase the number of epochs
Why wrong: More epochs increase training time.
Use a smaller image size
Why wrong: Smaller images may reduce accuracy.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use SageMaker's distributed data parallelism
SageMaker's distributed data parallelism splits the training data across multiple GPUs or instances, allowing each worker to process a different subset of the data simultaneously. This reduces the wall-clock time per epoch by parallelizing the computation, which directly addresses the 'taking too long' issue for deep learning image classification models.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use SageMaker's distributed data parallelism
Why this is correct
Distributed training speeds up training by parallelizing across GPUs.
Related concept
Read the scenario before looking for a memorised answer.
Use SageMaker Neo to compile the model
Why it's wrong here
Neo is for inference optimization, not training.
Increase the number of epochs
Why it's wrong here
More epochs increase training time.
Use a smaller image size
Why it's wrong here
Smaller images may reduce accuracy.
Common exam traps
AWS often tests the distinction between training acceleration (distributed data parallelism) and inference optimization (Neo), leading candidates to mistakenly choose Neo for training speed improvements.
Detailed technical explanation
SageMaker's distributed data parallelism uses the Horovod or SageMaker's own AllReduce algorithm to synchronize gradients across workers after each batch. Under the hood, it leverages NVIDIA NCCL for high-speed GPU-to-GPU communication, which can achieve near-linear scaling on large clusters. In real-world scenarios, this is critical for training models like ResNet-50 on ImageNet, where training on a single GPU could take weeks but can be reduced to hours with 64 GPUs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use SageMaker's distributed data parallelism — SageMaker's distributed data parallelism splits the training data across multiple GPUs or instances, allowing each worker to process a different subset of the data simultaneously. This reduces the wall-clock time per epoch by parallelizing the computation, which directly addresses the 'taking too long' issue for deep learning image classification models.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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.
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