AI0-001 AI Infrastructure and Technologies Practice Question
A data scientist is using PyTorch to train a custom NLP model. The training is slow on a single GPU. They want to speed up training by using multiple GPUs on a single machine. Which PyTorch feature 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
✓
torch.nn.DataParallel
DataParallel (or DistributedDataParallel) is PyTorch's built-in feature to split batches across multiple GPUs. It is straightforward for single-machine multi-GPU 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.
- ✗
TorchScript tracing
Why it's wrong here
TorchScript is for model serialization and optimization, not parallelism.
- ✓
torch.nn.DataParallel
Why this is correct
DataParallel automatically splits input across GPUs and aggregates gradients; it's the simplest multi-GPU approach.
- ✗
torch.optim.SGD
Why it's wrong here
SGD is an optimizer, not a parallelism tool.
- ✗
PyTorch Lightning's zero_grad function
Why it's wrong here
zero_grad is for gradients, not multi-GPU parallelism.
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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.