- A
TPU
Why wrong: TPUs are custom ASICs by Google for accelerating TensorFlow workloads, but they are less commonly available and not as versatile as GPUs for general deep learning.
- B
GPU
GPUs contain thousands of cores that can perform parallel matrix operations, greatly accelerating training of deep learning models.
- C
NPU
Why wrong: NPUs are specialized for inference tasks on edge devices, not for training large models.
- D
CPU
Why wrong: CPUs are optimized for sequential processing and are not efficient for the massive parallelism required in deep learning training.
Question 702 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist needs to train a deep learning model on a large image dataset. Which hardware component is specifically designed to accelerate deep learning training workloads?
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
✓
GPU
B (GPU) is correct because GPUs contain thousands of parallel cores designed for matrix operations, which are fundamental to deep learning training. They significantly accelerate the forward and backward passes of neural networks compared to CPUs, making them the standard choice for training large image datasets.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
TPU
Why it's wrong here
TPUs are custom ASICs by Google for accelerating TensorFlow workloads, but they are less commonly available and not as versatile as GPUs for general deep learning.
- ✓
GPU
Why this is correct
GPUs contain thousands of cores that can perform parallel matrix operations, greatly accelerating training of deep learning models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
NPU
Why it's wrong here
NPUs are specialized for inference tasks on edge devices, not for training large models.
- ✗
CPU
Why it's wrong here
CPUs are optimized for sequential processing and are not efficient for the massive parallelism required in deep learning training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between training accelerators (GPUs) and inference accelerators (NPUs/TPUs), where candidates mistakenly choose TPU or NPU because they associate 'AI' with any specialized hardware, but the question explicitly asks for 'deep learning training' which is GPU-dominated.
Detailed technical explanation
How to think about this question
GPUs leverage CUDA cores (NVIDIA) or Stream Processors (AMD) to perform thousands of simultaneous floating-point operations, which directly maps to the tensor operations in deep learning frameworks like TensorFlow and PyTorch. The key advantage comes from memory bandwidth and parallel execution, where a single GPU can reduce training time from weeks to days compared to a CPU. In practice, data scientists often use multi-GPU setups with NVLink or PCIe interconnects to scale training across large datasets like ImageNet.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: GPU — B (GPU) is correct because GPUs contain thousands of parallel cores designed for matrix operations, which are fundamental to deep learning training. They significantly accelerate the forward and backward passes of neural networks compared to CPUs, making them the standard choice for training large image datasets.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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