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. 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 on a large dataset using Amazon SageMaker. The training job is taking too long. Which action would MOST likely reduce training time without sacrificing model accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable data parallelism across multiple GPUs
Option C is correct because enabling data parallelism across multiple GPUs distributes the training workload across several devices, allowing larger batch sizes and faster gradient computation per epoch. Amazon SageMaker's distributed training libraries (e.g., SageMaker Data Parallelism) use all-reduce algorithms to synchronize gradients efficiently, which reduces wall-clock training time without altering the model architecture or loss function, thus preserving accuracy.
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.
✗
Use a smaller instance type for training
Why it's wrong here
Smaller instances have less compute power, leading to longer training times.
✗
Implement early stopping with a low patience value
Why it's wrong here
Early stopping can prevent overfitting but may end training too early, reducing accuracy.
✓
Enable data parallelism across multiple GPUs
Why this is correct
Data parallelism distributes data across GPUs, reducing training time while preserving accuracy.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the number of epochs by half
Why it's wrong here
Reducing epochs can lower training time but often at the cost of accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse reducing training time with reducing computational load (e.g., smaller instance or fewer epochs), but the question specifically requires maintaining accuracy, which distributed parallelism achieves by leveraging more hardware rather than cutting corners in the training process.
Detailed technical explanation
How to think about this question
Data parallelism in SageMaker leverages the Horovod or SageMaker Data Parallel (SDP) library, which uses ring-allreduce to synchronize gradients across GPUs without requiring a parameter server. This approach scales nearly linearly with the number of GPUs for large models and datasets, but it requires careful tuning of batch size and learning rate (e.g., linear scaling rule) to maintain model accuracy. In practice, for very large models like GPT or BERT, pipeline parallelism or model parallelism may be needed alongside data parallelism to avoid memory bottlenecks.
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: Enable data parallelism across multiple GPUs — Option C is correct because enabling data parallelism across multiple GPUs distributes the training workload across several devices, allowing larger batch sizes and faster gradient computation per epoch. Amazon SageMaker's distributed training libraries (e.g., SageMaker Data Parallelism) use all-reduce algorithms to synchronize gradients efficiently, which reduces wall-clock training time without altering the model architecture or loss function, thus preserving accuracy.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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
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