Courseiva
AI Infrastructure and TechnologiesmediumMultiple ChoiceObjective-mapped

AI0-001 AI Infrastructure and Technologies Practice Question

A machine learning team is training a large transformer model on a text corpus. They need to reduce training time while maintaining model accuracy. Which hardware configuration would be MOST effective for this task?

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 cluster of GPUs with data parallelism

GPUs are optimized for the parallel computations required in deep learning training, offering significant speedups over CPUs. TPUs are also effective but less accessible and more specialized. The question specifies 'most effective' for training a transformer model, which aligns with GPU acceleration.

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 a high-core-count CPU with large RAM

    Why it's wrong here

    CPUs are inefficient for parallel matrix operations common in deep learning; training would be very slow.

  • Use a cluster of GPUs with data parallelism

    Why this is correct

    GPUs accelerate parallel tensor operations, and data parallelism distributes batches across multiple GPUs, significantly reducing training time.

  • Use a single GPU with model parallelism

    Why it's wrong here

    Model parallelism on a single GPU fails to reduce training time because it partitions the model across the device’s own memory, introducing sequential computation and communication overhead that does not scale throughput. It is tempting because it enables fitting a transformer that exceeds a single GPU’s VRAM, but the correct approach for speed is data parallelism across multiple GPUs, which processes distinct micro-batches concurrently.

  • Use a single TPU with model parallelism

    Why it's wrong here

    TPU is specialized but less flexible; single TPU may not provide enough compute for large models, and model parallelism adds complexity.

About these practice questions

Courseiva writes every AI0-001 question from scratch — 754 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

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.