- A
Disable automatic model tuning.
Why wrong: Not related to training time.
- B
Use a larger instance type with more vCPUs.
Why wrong: May not scale linearly and is costlier.
- C
Use SageMaker's distributed training with multiple instances.
Parallel processing reduces training time.
- D
Reduce the number of epochs.
Why wrong: Would reduce accuracy.
Question 812 of 1,755
MLS-C01 Distributed Training (Data Parallelism) Practice Question
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: distributed Training (Data Parallelism). 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 company is using SageMaker to train a linear regression model on a dataset that fits into memory on a single instance. The training job is taking longer than expected. The data scientist wants to reduce training time without changing the algorithm. Which approach is most effective?
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
✓
Use SageMaker's distributed training with multiple instances.
Option C is correct because SageMaker's distributed training with multiple instances performs data parallelism, splitting the dataset across instances to train concurrently, which reduces training time for data that fits in memory. Option A is wrong because disabling automatic model tuning (hyperparameter tuning) does not speed up training; it only stops searching for optimal hyperparameters. Option B is wrong because while a larger instance with more vCPUs can help, distributed training scales better and is more cost-effective for this scenario. Option D is wrong because reducing the number of epochs would likely underfit the model, hurting accuracy.
Key principle: Distributed Training (Data Parallelism)
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable automatic model tuning.
Why it's wrong here
Not related to training time.
- ✗
Use a larger instance type with more vCPUs.
Why it's wrong here
May not scale linearly and is costlier.
- ✓
Use SageMaker's distributed training with multiple instances.
Why this is correct
Parallel processing reduces training time.
Related concept
Distributed Training (Data Parallelism)
- ✗
Reduce the number of epochs.
Why it's wrong here
Would reduce accuracy.
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Distributed Training (Data Parallelism)
- SageMaker Managed Training
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
Distributed Training (Data Parallelism)
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review distributed Training (Data Parallelism), then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Distributed Training (Data Parallelism).
What is the correct answer to this question?
The correct answer is: Use SageMaker's distributed training with multiple instances. — Option C is correct because SageMaker's distributed training with multiple instances performs data parallelism, splitting the dataset across instances to train concurrently, which reduces training time for data that fits in memory. Option A is wrong because disabling automatic model tuning (hyperparameter tuning) does not speed up training; it only stops searching for optimal hyperparameters. Option B is wrong because while a larger instance with more vCPUs can help, distributed training scales better and is more cost-effective for this scenario. Option D is wrong because reducing the number of epochs would likely underfit the model, hurting accuracy.
What should I do if I get this MLS-C01 question wrong?
Review distributed Training (Data Parallelism), then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Distributed Training (Data Parallelism)
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Last reviewed: Jun 20, 2026
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