AI0-001 AI Infrastructure and Technologies Practice Question
A data scientist is training a large language model on a custom dataset using PyTorch on AWS. The training is taking too long due to GPU memory constraints. The team wants to use multiple GPUs across instances with minimal code changes. Which AWS service 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
✓
Amazon SageMaker with distributed training libraries
SageMaker distributed training libraries support data parallelism and model parallelism with minimal code changes, enabling multi-GPU training across instances efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Elastic Fabric Adapter (EFA)
Why it's wrong here
EFA is a network interface for high-performance computing but requires manual setup; it's not a managed training service.
- ✓
Amazon SageMaker with distributed training libraries
Why this is correct
SageMaker's distributed libraries (e.g., SageMaker Data Parallelism) enable multi-GPU training with minimal code changes.
- ✗
AWS Batch with GPU instances
Why it's wrong here
AWS Batch schedules jobs but does not natively handle distributed training with PyTorch.
- ✗
AWS ParallelCluster with Slurm
Why it's wrong here
ParallelCluster is for HPC workloads and requires significant configuration for ML training.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.