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What is the NCP-AI Exam?

The Nutanix Certified Professional – Artificial Intelligence (NCP-AI) 6.10 exam validates your ability to deploy, manage, and troubleshoot AI workloads within the Nutanix ecosystem. It focuses on Nutanix Enterprise AI (NAI) and tests how effectively you can integrate modern Generative AI (GenAI) applications and agents into enterprise environments. This certification demonstrates that you can work with AI infrastructure at a practical level - handling everything from installation and configuration to performance optimization and issue resolution.

Who is the NCP-AI Exam For?

The NCP-AI certification is designed for IT professionals who already have a solid foundation in infrastructure and are moving into AI-driven environments. It is ideal for:

● System administrators and engineers working with virtual infrastructure

● Cloud engineers with experience in cloud-native technologies

● DevOps professionals integrating AI services into applications

● IT professionals with Kubernetes and Linux CLI experience

Typically, candidates should have:

● Around 3 years of virtualization experience

● At least 1 year of cloud-native experience

● Familiarity with Kubernetes (CKA-level knowledge), GPUs, and IaaS platforms

Exam Overview

Here's a quick breakdown of the NCP-AI 6.10 exam:

Questions: 75 (multiple-choice & multiple-response)

Duration: 120 minutes

Languages: English, Japanese

Price: $200

Passing Score: 3000

The exam evaluates both conceptual understanding and hands-on operational skills in managing AI environments on Nutanix.

Skills Measured

The exam is divided into five key domains:

1. Deploy a Nutanix Enterprise AI Environment

Validate prerequisites

Install NAI components

Configure DNS, URLs, and certificates

2. Configure a Nutanix Enterprise AI Environment

Onboard users

Import Large Language Models (LLMs)

Create and manage API endpoints and keys

3. Perform Day 2 Operations

Optimize performance based on metrics

Monitor usage and detect anomalies

Select appropriate LLMs for better output

4. Troubleshoot a Nutanix Enterprise AI Environment

Resolve performance and resource issues

Fix cluster health problems

Troubleshoot model imports and endpoints

5. Connect Applications to NAI

Integrate applications with endpoints

Validate configurations

Monitor endpoint usage and metrics

How to Prepare for the NCP-AI Exam?

Preparation for the NCP-AI exam should combine hands-on practice, conceptual learning, and real-world scenarios:

Start by building a strong understanding of Nutanix Cloud Infrastructure (NCI) and AI-related services. Then, focus on deploying and managing Nutanix Enterprise AI in a lab environment if possible.

You should also:

● Practice working with LLMs and API endpoints

● Strengthen your Linux CLI and Kubernetes skills

● Learn how to monitor and optimize AI workloads

● Study troubleshooting techniques for performance and cluster issues

Combining documentation study with practical exercises will significantly improve your readiness.

How to Use NCP-AI Practice Questions?

Practice questions are most effective when used strategically - not just for memorization.

Here's how to use them properly:

Start with assessment: Use practice questions early to identify weak areas

Learn from explanations: Focus on understanding why an answer is correct

Simulate exam conditions: Practice under timed conditions

Track progress: Revisit difficult topics and measure improvement

This approach helps reinforce both knowledge and exam confidence.

Practice Questions for NCP-AI Exam

Practice questions play a crucial role in preparing for the NCP-AI exam. They help you become familiar with the exam format, improve time management, and identify knowledge gaps. More importantly, they expose you to real-world scenarios similar to those you will encounter in the exam, allowing you to develop problem-solving skills and practical understanding. Consistent practice ensures you are not only prepared to answer questions correctly but also to apply your knowledge effectively in real environments.

Question#1

An administrator notices increased model inference latency and frequent timeout errors in a Nutanix AI deployment during peak usage.
What is the most effective action to troubleshoot and resolve the performance issue?

A. Reduce the number of API keys to limit external access to the model.

B. Restart the Nutanix Kubernetes cluster to clear cached memory and reset service states.

C. Disable logging and monitoring tools to free up system resources.

D. Analyze resource metrics and scale out the model service to handle increased load.

Question#2

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What does hibernating an endpoint do?

A. It deletes all the resources from the endpoint without affecting the LL

B. It pauses the endpoint and releases compute resources without deleting the endpoint.

C. It freezes the activity of the endpoint to make edits to the endpoint.

D. It optimizes resource usage and deletes the endpoint for new endpoints to be created.

Question#3

An administrator is managing a Nutanix AI cluster used for NLP (Natural Language Processing) training. A data scientist reports that training jobs intermittently stall and fail to complete within the expected time window. The administrator reviews the performance data for the VM hosting the job and finds:
The VM is configured with passthrough access to a dedicated GPU
Memory ballooning is active, and swap usage is increasing
CPU utilization is moderate (~60%)
GPU utilization is stable and high (~85%)
The VM has 8 vCPUs and 24 GB of RAM assigned
NCC shows no hardware or driver issues
What is the most appropriate optimization to improve workload stability and performance?

A. Reduce the number of vCPUs allocated to lower the CPU scheduling overhead.

B. Add additional vGPUs to the VM to reduce processing time.

C. Increase the VM's RAM to eliminate memory ballooning and swap usage.

D. Disable GPU passthrough and use a shared vGPU profile instead.

Question#4

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An Accounting Department is thrilled with the RAG Application that the Application & Data Science Teams recently rolled out. However, they provided some feedback that sometimes (approximately 20% of the time), the documents retrieved are not relevant to their prompts or are too generic.
During development, there was extensive testing between models to make sure the best possible model was selected. The Accounting Department emphasizes that when the responses use the right documents, the results are very good and they are pleased with the completeness, accuracy, and coherence of those responses.
What would be a way to address the irrelevant RAG results without having to rebuild the entire workflow?

A. Replace the embedding model with a larger, more general-purpose language model to improve document retrieval.

B. Fine-tune the Large Language Model on a broader dataset to enable it to generate more relevant responses.

C. Implement a rerank model as a post retrieval step to re-order initially retrieved documents based on query-document relevance.

D. Significantly expand the document knowledge base by ingesting a much larger volume of financial reports.

Question#5

When licensing Nutanix Enterprise AI, which license is required to create an inference endpoint?

A. GPU GB

B. vCPU

C. NUS Pro

D. NKP Ultimate

Disclaimer

This page is for educational and exam preparation reference only. It is not affiliated with Nutanix, Nutanix Certified Professional (NCP), or the official exam provider. Candidates should refer to official documentation and training for authoritative information.

Exam Code: NCP-AIQ & A: 75 Q&As Updated: 2026-05-25

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