MLS-C01 Modeling Practice Question
A data scientist is using SageMaker to train a deep learning model for image classification. The training job is taking too long. Which approach can reduce training time?
⚠ Common exam trap
AWS often tests the distinction between training acceleration (distributed data parallelism) and inference optimization (Neo), leading candidates to mistakenly choose Neo for training speed improvements.
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 data parallelism
SageMaker's distributed data parallelism splits the training data across multiple GPUs or instances, allowing each worker to process a different subset of the data simultaneously. This reduces the wall-clock time per epoch by parallelizing the computation, which directly addresses the 'taking too long' issue for deep learning image classification models.
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 SageMaker's distributed data parallelism
Why this is correct
Distributed training speeds up training by parallelizing across GPUs.
- ✗
Use SageMaker Neo to compile the model
Why it's wrong here
Neo is for inference optimization, not training.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs increase training time.
- ✗
Use a smaller image size
Why it's wrong here
Smaller images may reduce 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.