Question 194 of 1,672
MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to train a deep learning model on a large dataset. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which action should they take?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that simply adjusting hyperparameters like learning rate or batch size can solve performance issues, when the correct answer is to leverage distributed computing resources that SageMaker provides natively.
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
SageMaker's distributed training with multiple instances splits the dataset and model computations across several machines, enabling parallel processing that significantly reduces wall-clock training time. This approach leverages data parallelism or model parallelism without altering the model architecture, directly addressing the need for faster training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate by a factor of 10
Why it's wrong here
Abrupt increase may cause divergence.
- ✓
Use SageMaker's distributed training with multiple instances
Why this is correct
Distributed training parallelizes the workload.
- ✗
Reduce the number of epochs
Why it's wrong here
May degrade model performance.
- ✗
Reduce the batch size
Why it's wrong here
Smaller batch size usually increases training time.
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Last reviewed: Jun 11, 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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