This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to speed up training by using distributed training across multiple GPUs. Which SageMaker feature or configuration should the data scientist use?
Why wrong: SageMaker Debugger monitors training jobs but does not accelerate training.
B
Model parallelism in SageMaker
Why wrong: Model parallelism splits the model across devices, but it is typically used for very large models that do not fit in memory, not for speeding up training of standard image classifiers.
C
SageMaker hyperparameter tuning
Why wrong: Hyperparameter tuning runs multiple training jobs with different hyperparameters but does not speed up a single training job.
D
SageMaker Data Parallelism library
The SageMaker Data Parallelism library distributes data across multiple GPUs, reducing training time for large datasets.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Data Parallelism library
Option D is correct because the SageMaker Data Parallelism library is specifically designed to distribute training across multiple GPUs by splitting the input data across workers, which reduces per-GPU computation time and accelerates training for deep learning models. This library uses optimized all-reduce algorithms (e.g., Ring AllReduce) to synchronize gradients efficiently, making it ideal for speeding up image classification tasks that are data-intensive.
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.
✗
SageMaker Debugger
Why it's wrong here
SageMaker Debugger monitors training jobs but does not accelerate training.
✗
Model parallelism in SageMaker
Why it's wrong here
Model parallelism splits the model across devices, but it is typically used for very large models that do not fit in memory, not for speeding up training of standard image classifiers.
✗
SageMaker hyperparameter tuning
Why it's wrong here
Hyperparameter tuning runs multiple training jobs with different hyperparameters but does not speed up a single training job.
✓
SageMaker Data Parallelism library
Why this is correct
The SageMaker Data Parallelism library distributes data across multiple GPUs, reducing training time for large datasets.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse model parallelism (splitting the model) with data parallelism (splitting the data), and incorrectly choose model parallelism when the scenario clearly describes a training speed issue solvable by distributing data across GPUs.
Detailed technical explanation
How to think about this question
The SageMaker Data Parallelism library implements a custom all-reduce algorithm that overlaps gradient computation with communication, reducing idle time during synchronization. In practice, for image classification with large datasets like ImageNet, this can yield near-linear speedups when scaling from 1 to 8 GPUs, as the library automatically partitions mini-batches and handles gradient averaging without requiring manual code changes beyond wrapping the model with `sagemaker.distributed.DistributedDataParallel`.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SageMaker Data Parallelism library — Option D is correct because the SageMaker Data Parallelism library is specifically designed to distribute training across multiple GPUs by splitting the input data across workers, which reduces per-GPU computation time and accelerates training for deep learning models. This library uses optimized all-reduce algorithms (e.g., Ring AllReduce) to synchronize gradients efficiently, making it ideal for speeding up image classification tasks that are data-intensive.
What should I do if I get this MLS-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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Question Discussion
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