MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a deep learning model on a GPU instance. The training job is taking too long. Which action would MOST likely reduce training time?
⚠ Common exam trap
Many candidates confuse reducing mini-batch size (Option A) with improving training speed, but in GPU-accelerated deep learning, larger batch sizes better utilize GPU parallelism and reduce the number of iterations, making a larger instance the more effective solution.
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 a larger GPU instance type, such as p3.16xlarge
Using a larger GPU instance like p3.16xlarge provides significantly more GPU memory, CUDA cores, and memory bandwidth, which allows for larger batch sizes and more efficient parallel processing of matrix operations. This directly reduces training time for deep learning models by enabling faster forward and backward passes through the network, especially when the model is large enough to fully utilize the additional GPU resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the mini-batch size
Why it's wrong here
Smaller batch size can make training slower per epoch.
- ✗
Use distributed data parallelism across multiple smaller instances
Why it's wrong here
Distributed training may have overhead and not always reduce time.
- ✓
Use a larger GPU instance type, such as p3.16xlarge
Why this is correct
More powerful GPU accelerates training.
- ✗
Reduce the number of epochs
Why it's wrong here
Reducing epochs may degrade model accuracy.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.