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Updated: Nov 02, 2026

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NVIDIA NCP-AIO Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA-Certified Professional: AI Operations Exam
Exam Number:NCP-AIO
Passing Score:Pass/Fail (not officially disclosed)
Exam Price:$500 USD
Certificate Validity Period:2 years
Available Languages:English
Related Certifications:NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO)
NVIDIA-Certified Professional: AI Networking (NCP-AIN)
NVIDIA-Certified Professional: AI Infrastructure (NCP-AII)
Exam Format:Multiple choice, Scenario-based, Hands-on lab exercises
Real Exam Qty:30–75
Exam Duration:120 minutes
Recommended Training:NVIDIA AI Operations Training
Exam Registration:NVIDIA Certification Portal
Certiverse Exam Platform
Sample Questions:NVIDIA NCP-AIO Sample Questions
Exam Way:Online remote proctored exam
Pre Condition:Recommended: 2–3 years of experience managing AI infrastructure, GPU systems, or data center operations; familiarity with Kubernetes, containers, and NVIDIA software stack
Official Syllabus URL:https://www.nvidia.com/en-us/learn/certification/ai-operations-professional/

NVIDIA NCP-AIO Exam Syllabus Topics:

TopicDetails
Topic 1
  • Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.
Topic 2
  • Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.
Topic 3
  • Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
Topic 4
  • Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.

Reference: https://www.nvidia.com/en-us/learn/certification/ai-operations-professional/

NVIDIA AI Operations Sample Questions:

Question #1

An administrator is troubleshooting issues with an NVIDIA Unified Fabric Manager Enterprise (UFM) installation and notices that the UFM server is unable to communicate with InfiniBand switches.
What step should be taken to address the issue?

  • A. Reboot the UFM server to refresh network connections.
  • B. Install additional GPUs in the UFM server to boost connectivity.
  • C. Verify the subnet manager configuration on the InfiniBand switches.
  • D. Disable the firewall on the UFM server to allow communication.
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Correct Answer: C  🗳️

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Question #2

A cloud engineer is looking to deploy a digital fingerprinting pipeline using NVIDIA Morpheus and the NVIDIA AI Enterprise Virtual Machine Image (VMI).
Where would the cloud engineer find the VMI?

  • A. Azure, Google, Amazon Marketplaces
  • B. Developer Forums
  • C. Github and Dockerhub
  • D. NVIDIA NGC
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Correct Answer: A  🗳️

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Question #3

You are tasked with deploying a DOCA service on an NVIDIA BlueField DPU in an air-gapped data center environment. The DPU has the required BlueField OS version (3.9.0 or higher) installed, and you have access to the necessary container image from NVIDIA's NGC catalog.
However, you need to ensure that the deployment process is successful without an internet connection.
Which of the following steps should you take to deploy the DOCA service on the DPU?

  • A. Install Docker on the DPU, pull the container directly from NGC, and run it using `docker run' with appropriate environment variables.
  • B. Use the host system's Docker engine to pull the container image and deploy it on the DPU via SSH.
  • C. Manually download the container image and YAML file beforehand, transfer them to the DPU, and deploy using Kubernetes with standalone Kubelet.
  • D. Pull the container image from NGC using Docker and modify the YAML file before deployment.
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Correct Answer: C  🗳️

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Question #4

You are deploying AI applications at the edge and want to ensure they continue running even if one of the servers at an edge location fails.
How can you configure NVIDIA Fleet Command to achieve this?

  • A. Enable high availability for edge clusters.
  • B. Configure Fleet Command's multi-instance GPU (MIG) to handle failover.
  • C. Set up over-the-air updates to automatically restart failed applications.
  • D. Use Secure NFS support for data redundancy.
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Correct Answer: A  🗳️

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Question #5

Which logging practice is most important in AI operations to enable debugging, auditing, and performance tracking of machine learning models in production systems?

  • A. Random logging
  • B. Manual logging
  • C. Minimal logging
  • D. Structured logging
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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