- Exam Breakdown
- Domain Breakdown
- Access Breakdown
Exam Format
- Exam Code: NCP-AI
- Certification Level: Professional
- Exam Duration: 120 minutes
- Passing Score: 3000 on a scaled score range of 1000–6000
- Unscored Content: Nutanix does not officially disclose separate unscored questions for this exam. Candidates should treat all questions as scored unless stated otherwise during the exam.
Exam Details
- Question Types: Multiple choice and multiple response
- Number of Questions: 75 questions
- Hands-On Questions: The exam does not include hands-on lab questions. It is a proctored exam consisting of multiple-choice and multiple-response questions based on real-world Nutanix Enterprise AI administration scenarios.
Exam Policies
- Offline Proctoring: The exam can be taken in person at select test centres. Candidates must follow the test centre check-in, identification, and scheduling requirements.
- Online Proctoring: The exam can be taken through remote online proctoring using a secure, monitored testing environment. Candidates must meet system, ID, workspace, and proctoring requirements.
- Waiting Period: A 7-calendar-day waiting period is required between failed exam attempts.
- Retake Fee: Each retake attempt must be paid for individually unless an official Nutanix voucher or promotion is applied.
Certification Validity and Renewal
- Validity: 3 years
- Renewal Options: Recertify by renewing the existing certification, passing another active NCP-level exam, or passing a higher-level Nutanix certification exam such as NCM, depending on Nutanix’s current recertification policy.
Exam Fee
- Base Fee: $200 USD per attempt
- Taxes: Country-specific taxes may apply.
- Example: In India, 18% tax applies, making the total $236 USD ($200 + $36 tax)
Prerequisites
There are no formal prerequisites for taking the Nutanix Certified Professional - Artificial Intelligence exam. However, it is recommended to have:
- At least 3 years of virtual infrastructure experience
- At least 1 year of experience working with cloud-native technologies
- Experience using the Linux command-line interface
- Foundational knowledge of virtual machines, hypervisors, and virtual networking
- Knowledge of Nutanix Cloud Infrastructure (NCI)
- Understanding of cloud-based Infrastructure-as-a-Service (IaaS)
- Familiarity with GPUs and Nutanix Unified Storage
- Certified Kubernetes Administrator (CKA)-level knowledge
- Completion of the Nutanix Enterprise AI Administration (NAIA) course is recommended
Exam Topics
- Deploy a Nutanix Enterprise AI Environment: Validate installation prerequisites, install Nutanix Enterprise AI components, configure DNS, set up URLs, and manage required certificates
- Configure a Nutanix Enterprise AI Environment: Onboard users, import Large Language Models (LLMs), create endpoints, create and apply API endpoint keys, and deliver endpoints to consumers
- Perform Day 2 Operations: Prepare requirements for connecting applications, interpret performance details, optimize performance, monitor access activity, and select the appropriate LLM to improve output quality
- Troubleshoot a Nutanix Enterprise AI Environment: Troubleshoot and resolve performance issues, resource utilization issues, health check failures, model import issues, and endpoint creation problems
- Connect Applications to a Nutanix Enterprise AI Environment: Configure and validate applications with endpoints and check endpoint metrics related to application usage
For a detailed breakdown and official study guide, feel free to contact us!
Intended Audience
The Nutanix Certified Professional - Artificial Intelligence certification is ideal for professionals who deploy, configure, manage, and troubleshoot Nutanix Enterprise AI environments, including roles such as:
- AI Infrastructure Engineer
- Cloud Engineer
- Platform Engineer
- DevOps Engineer
- Kubernetes Administrator
- Systems Administrator
- Infrastructure Architect
- Solution Architect
- Site Reliability Engineer
Career Impact
Jobs You Can Get:
- AI Infrastructure Engineer,
- Cloud Engineer,
- Platform Engineer,
- DevOps Engineer,
- Kubernetes Administrator,
- Systems Administrator,
- Solution Architect,
- Infrastructure Architect,
- Site Reliability Engineer, etc.
Average Salary: Varies by country —
- U.S.: $100,000–$160,000 USD,
- India: ₹8,00,000–₹25,00,000 INR,
- United Kingdom: £50,000–£90,000 GBP,
- UAE: 150,000–280,000 AED per year.
Why It’s Valuable:
- It validates practical skills in enterprise AI infrastructure, Nutanix Enterprise AI administration, GenAI application integration, LLM endpoint management, Kubernetes-related operations, troubleshooting, and AI platform optimization.
Exam Mode
The exam is proctored and can be taken either:
- In-person at select authorized test centres
- Online through remote proctoring
Exam Booking Link
- Book your Nutanix Certified Professional - Artificial Intelligence Exam via Nutanix University — Click here (https://www.nutanix.com/support-services/training-certification/certifications/certification-details-nutanix-certified-professional-artificial-intelligence)
Once you pass the exam:
- Earn the Nutanix Certified Professional - Artificial Intelligence certification
- Receive a digital badge from Credly
- Access certification benefits such as use of the NCP-AI logo, Nutanix Certification store access, discounts on Nutanix events, and early access to educational programs
- Log in to Nutanix University or Credly to access your certification record and digital badge
Offers
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Top Reasons to Choose
Nutanix Certified Professional - Artificial Intelligence
High Demand for Enterprise AI Infrastructure Skills
Organizations adopting GenAI need skilled professionals who can deploy, configure, and operate secure AI infrastructure. This certification validates practical Nutanix Enterprise AI and platform administration skills.
Strong Focus on GenAI and LLM Operations
The exam covers real-world AI platform tasks such as importing LLMs, creating endpoints, applying API keys, monitoring endpoint usage, optimizing model performance, and troubleshooting NAI environments.
Career Growth and Nutanix Recognition
As an official Nutanix Professional-level certification, NCP-AI helps demonstrate AI infrastructure expertise and supports career growth in AI operations, platform engineering, cloud engineering, DevOps, and SRE roles.
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FAQ
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Who should take the Nutanix Certified Professional - Artificial Intelligence exam?
The NCP-AI exam is ideal for AI infrastructure engineers, cloud engineers, platform engineers, DevOps engineers, Kubernetes administrators, systems administrators, solution architects, infrastructure architects, and SRE professionals who work with Nutanix Enterprise AI environments.
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How difficult is the NCP-AI exam?
The difficulty level of the NCP-AI exam is considered moderate to challenging. Candidates should understand virtual infrastructure, Nutanix Cloud Infrastructure, Linux CLI, Kubernetes concepts, cloud-native technologies, GPUs, Nutanix Unified Storage, LLM imports, API endpoints, model serving, monitoring, optimization, and troubleshooting.
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Why does Nutanix offer the Artificial Intelligence certification?
Nutanix offers this certification to validate the skills required to install, configure, optimize, troubleshoot, and operate Nutanix Enterprise AI. It helps organizations identify professionals who can manage enterprise AI platforms and integrate GenAI applications and agents with NAI.
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What tools and resources can be used to prepare for the NCP-AI exam?
Candidates can prepare using official Nutanix resources, including: • Nutanix Certified Professional - Artificial Intelligence certification page • Nutanix Enterprise AI Administration (NAIA) course • Nutanix University • Nutanix Enterprise AI documentation • Nutanix Cloud Infrastructure documentation • Nutanix Unified Storage documentation • Kubernetes learning resources • Hands-on practice with NAI, Linux CLI, Kubernetes, GPUs, LLM endpoints, API keys, monitoring, and troubleshooting workflows
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Is the Nutanix Certified Professional - Artificial Intelligence certification still valuable in 2026?
Yes, the NCP-AI certification is valuable in 2026. Enterprise AI, GenAI applications, LLM operations, AI infrastructure, Kubernetes, and hybrid multicloud platforms continue to be important areas for organizations building and operating modern AI workloads.