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NVIDIA NCP-AIO : NVIDIA AI Operations

Exam Code: NCP-AIO

Exam Name: NVIDIA AI Operations

Updated: Oct 16, 2026

Q & A: 87 Questions and Answers

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NVIDIA NCP-AIO Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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 4
  • 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.

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

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NVIDIA AI Operations Sample Questions:

Question #1

A system administrator is looking to set up virtual machines in an HGX environment with NVIDIA Fabric Manager.
What three (3) tasks will Fabric Manager accomplish? (Choose three.)

  • A. Coordinates with the NVSwitch driver to train NVSwitch to NVSwitch NVLink interconnects.
  • B. Configures routing among NVSwitch ports.
  • C. Installs GPU operator
  • D. Installs vGPU driver as part of the Fabric Manager Package.
  • E. Coordinates with the GPU driver to initialize and train NVSwitch to GPU NVLink interconnects.
Reveal Solution  Discussion  0

Correct Answer: A,B,E  🗳️

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Question #2

A Fleet Command system administrator wants to create an organization user that will have the following rights:
For locations - read only
For Applications - read/write/admin
For Deployments - read/write/admin
For Dashboards - read only
What role should the system administrator assign to this user?

  • A. Fleet Command Supporter
  • B. Fleet Command Operator
  • C. Fleet Command Viewer
  • D. Fleet Command Admin
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?

  • A. ibtracert
  • B. ibping
  • C. ibstatus
  • D. ibnetdiscover
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

In an AI operations pipeline, which component is primarily responsible for continuously monitoring deployed models for performance degradation and automatically triggering retraining workflows when accuracy or latency metrics fall below predefined thresholds?

  • A. Data ingestion layer
  • B. Model monitoring system
  • C. Feature engineering module
  • D. Training scheduler
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

You are configuring cloudbursting for your on-premises cluster using BCM, and you plan to extend the cluster into both AWS and Azure.
What is a key requirement for enabling cloudbursting across multiple cloud providers?

  • A. You only need to configure credentials for one cloud provider, as BCM will automatically replicate them across other providers.
  • B. You need to set up a single set of credentials that works across both AWS and Azure for seamless integration.
  • C. BCM automatically detects and configures credentials for all supported cloud providers without requiring admin input.
  • D. You must configure separate credentials for each cloud provider in BCM to enable their use in the cluster extension process.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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