MLA-C01 ML Model Development Practice Question
A team is training a large deep learning model on SageMaker using a single ml.p3.16xlarge instance. Training is taking too long. They want to reduce time by distributing across multiple GPUs but are constrained by model size that does not fit in a single GPU memory. Which distributed training strategy should they use?
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
✓
Model parallelism using SageMaker distributed model parallelism
Model parallelism splits the model across multiple GPUs, which is needed when the model does not fit in a single GPU. Data parallelism replicates the model on each GPU and splits data, which requires the model to fit in each GPU's memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data parallelism using SageMaker distributed data parallelism
Why it's wrong here
Data parallelism requires the model to fit in each GPU's memory. Since the model does not fit, this is not feasible.
- ✗
Switch to a smaller instance type and use horizontal scaling
Why it's wrong here
Smaller instances have even less GPU memory, making the problem worse.
- ✗
Use multiple training jobs with hyperparameter tuning
Why it's wrong here
Hyperparameter tuning runs multiple trials but does not distribute a single model across GPUs; it does not solve the memory issue.
- ✓
Model parallelism using SageMaker distributed model parallelism
Why this is correct
Model parallelism partitions the model layers across GPUs, allowing training of models that exceed single GPU memory.
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.