AI0-001 AI Implementation and Operations Practice Question
A company serves a large language model (LLM) on a Kubernetes cluster. The inference latency is acceptable but the cost is high due to GPU usage. The model is 7 billion parameters and requires 16GB GPU memory. The team wants to reduce cost without increasing latency. Which strategy should they implement?
⚠ Common exam trap
CompTIA often tests the misconception that adding more hardware (Option B) or increasing batch size (Option A) always reduces cost, when in fact they increase resource usage and cost; the trap is that candidates overlook memory optimization techniques like quantization as a direct cost-reduction strategy.
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 quantization to reduce precision
Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases the GPU memory footprint from 16GB to approximately 4GB for a 7B parameter model. This directly lowers GPU cost per inference while maintaining acceptable latency, as the model can run on fewer or cheaper GPUs without increasing inference time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size for inference
Why it's wrong here
Larger batch size may increase latency and memory usage, potentially offsetting gains.
- ✗
Add more GPU nodes to distribute the load
Why it's wrong here
Adding more GPUs increases cost, not reduces.
- ✗
Switch to CPU-based inference
Why it's wrong here
CPU inference is slower for LLMs, increasing latency.
- ✓
Use model quantization to reduce precision
Why this is correct
Quantization reduces model size and memory, enabling more efficient GPU usage.
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 →
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.