- A
Use model quantisation to FP16
FP16 half-precision reduces memory and compute, lowering latency on compatible hardware.
- B
Add more GPUs and distribute the model
Why wrong: This adds cost and complexity; while it can reduce latency, it also changes the architecture (e.g., pipeline parallelism). It is not 'without changing model architecture'? Actually, distributing may not change model architecture, but it adds complexity and is not the simplest optimisation. The question asks for optimisation without changing architecture; quantisation is simpler and does not change the architecture.
- C
Increase the batch size to process multiple requests simultaneously
Why wrong: This can improve throughput but not per-request latency; it may even increase latency for the first request if batching is synchronous.
- D
Switch from GPU to CPU
Why wrong: CPU would be much slower for LLM inference.
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 team is using Hugging Face Transformers to serve an LLM via a REST API. They notice high latency during inference. The model is deployed on a single GPU. Which optimisation would reduce inference latency WITHOUT changing the model architecture?
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 model quantisation to FP16
FP16 quantization reduces the memory footprint and computational load of the model by using half-precision floating-point numbers, which allows the GPU to process more operations per second and reduces memory bandwidth usage. This directly lowers inference latency without altering the model's architecture, making it the correct choice for a single-GPU deployment.
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.
- ✓
Use model quantisation to FP16
Why this is correct
FP16 half-precision reduces memory and compute, lowering latency on compatible hardware.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more GPUs and distribute the model
Why it's wrong here
This adds cost and complexity; while it can reduce latency, it also changes the architecture (e.g., pipeline parallelism). It is not 'without changing model architecture'? Actually, distributing may not change model architecture, but it adds complexity and is not the simplest optimisation. The question asks for optimisation without changing architecture; quantisation is simpler and does not change the architecture.
- ✗
Increase the batch size to process multiple requests simultaneously
Why it's wrong here
This can improve throughput but not per-request latency; it may even increase latency for the first request if batching is synchronous.
- ✗
Switch from GPU to CPU
Why it's wrong here
CPU would be much slower for LLM inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between latency and throughput, and the trap here is that candidates confuse increasing batch size (which improves throughput) with reducing per-request latency, leading them to incorrectly select Option C.
Detailed technical explanation
How to think about this question
FP16 quantization leverages the GPU's tensor cores (e.g., NVIDIA's Volta and later architectures) which can perform mixed-precision operations at up to 8x the speed of FP32. However, care must be taken with activations and gradients to avoid numerical underflow; techniques like loss scaling are often used during training, but for inference, FP16 is generally safe for most LLMs. In real-world scenarios, this optimization can reduce latency by 2-4x on a single GPU, making it critical for real-time applications like chatbots.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
AI Infrastructure and Technologies — study guide chapter
Learn the concepts, then practise the questions
- →
AI Infrastructure and Technologies practice questions
Targeted practice on this topic area only
- →
All AI0-001 questions
1,000 questions across all exam domains
- →
CompTIA AI+ AI0-001 study guide
Full concept coverage aligned to exam objectives
- →
AI0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
AI Infrastructure and Technologies practice questions
Practise AI0-001 questions linked to AI Infrastructure and Technologies.
AI Security practice questions
Practise AI0-001 questions linked to AI Security.
AI Concepts and Foundations practice questions
Practise AI0-001 questions linked to AI Concepts and Foundations.
AI Concepts and Techniques practice questions
Practise AI0-001 questions linked to AI Concepts and Techniques.
Machine Learning and Deep Learning practice questions
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
AI Models and Data Engineering practice questions
Practise AI0-001 questions linked to AI Models and Data Engineering.
Implementing AI Solutions practice questions
Practise AI0-001 questions linked to Implementing AI Solutions.
AI Implementation and Operations practice questions
Practise AI0-001 questions linked to AI Implementation and Operations.
AI Security, Ethics and Governance practice questions
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
AI Governance and Ethics practice questions
Practise AI0-001 questions linked to AI Governance and Ethics.
CompTIA A+ hardware practice questions
Practise AI0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
Practice this exam
Start a free AI0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use model quantisation to FP16 — FP16 quantization reduces the memory footprint and computational load of the model by using half-precision floating-point numbers, which allows the GPU to process more operations per second and reduces memory bandwidth usage. This directly lowers inference latency without altering the model's architecture, making it the correct choice for a single-GPU deployment.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.