- A
ONNX runtime with a gRPC endpoint on a CPU-only node
Why wrong: ONNX runtime supports GPU, but this option specifies CPU-only, which would not meet the GPU acceleration requirement.
- B
Apache Spark with MLlib to serve the model in batch mode
Why wrong: Spark is for distributed data processing and batch inference, not real-time REST API serving with low latency.
- C
Docker container with a FastAPI application and Nvidia GPU support
Docker encapsulates the environment, FastAPI provides REST API capabilities, and Nvidia GPU support enables GPU acceleration for inference.
- D
Kubeflow Pipelines to deploy the model as a scheduled job
Why wrong: Kubeflow Pipelines is for orchestrating ML workflows, not for serving real-time inference APIs.
Question 137 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 deploy a PyTorch model to production with low-latency inference. The model must be served as a REST API and should support GPU acceleration. Which combination of tools is MOST suitable 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
✓
Docker container with a FastAPI application and Nvidia GPU support
Option C is correct because it combines Docker containerization with FastAPI for a lightweight REST API and NVIDIA GPU support (via nvidia-docker or NVIDIA Container Toolkit) to enable low-latency GPU-accelerated inference. This stack directly meets the requirements of low-latency inference, REST API serving, and GPU acceleration without unnecessary overhead.
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.
- ✗
ONNX runtime with a gRPC endpoint on a CPU-only node
Why it's wrong here
ONNX runtime supports GPU, but this option specifies CPU-only, which would not meet the GPU acceleration requirement.
- ✗
Apache Spark with MLlib to serve the model in batch mode
Why it's wrong here
Spark is for distributed data processing and batch inference, not real-time REST API serving with low latency.
- ✓
Docker container with a FastAPI application and Nvidia GPU support
Why this is correct
Docker encapsulates the environment, FastAPI provides REST API capabilities, and Nvidia GPU support enables GPU acceleration for inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Kubeflow Pipelines to deploy the model as a scheduled job
Why it's wrong here
Kubeflow Pipelines is for orchestrating ML workflows, not for serving real-time inference APIs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between batch/offline processing tools (like Spark or Kubeflow Pipelines) and real-time serving frameworks, leading candidates to confuse orchestration or batch tools with low-latency inference solutions.
Detailed technical explanation
How to think about this question
FastAPI leverages Python's asyncio for non-blocking I/O, making it highly efficient for concurrent REST requests, while the NVIDIA Container Toolkit (nvidia-docker2) mounts the CUDA driver and runtime into the container, allowing PyTorch to access GPU devices directly via CUDA. In production, you would typically pair this with a production-grade ASGI server like Uvicorn or Gunicorn with Uvicorn workers to handle multiple requests and further reduce latency.
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: Docker container with a FastAPI application and Nvidia GPU support — Option C is correct because it combines Docker containerization with FastAPI for a lightweight REST API and NVIDIA GPU support (via nvidia-docker or NVIDIA Container Toolkit) to enable low-latency GPU-accelerated inference. This stack directly meets the requirements of low-latency inference, REST API serving, and GPU acceleration without unnecessary overhead.
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
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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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