Exam Code: NCP-AIO
Exam Name: NVIDIA AI Operations
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional

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NCP-AIO

Updated: Nov 10, 2026

No. of Questions: 87 Questions & Answers with Testing Engine

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NVIDIA NCP-AIO Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA Certified Professional: AI Operations (NCP-AIO)
Exam Number:NCP-AIO
Available Languages:English
Recommended Training:NVIDIA Deep Learning Institute (DLI)
NVIDIA Training Courses
Exam Registration:NVIDIA Certification Portal
Sample Questions:NVIDIA NCP-AIO Sample Questions
Exam Way:Likely online proctored and/or authorized testing center delivery (NVIDIA certification delivery varies by region and exam provider)
Official Syllabus URL:https://www.nvidia.com/en-us/training/certification/

NVIDIA NCP-AIO Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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.

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

NVIDIA AI Operations Sample Questions:

Question #1

Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?

  • A. The control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller- manager, while worker nodes run kubelet and kube-proxy.
  • B. Worker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.
  • C. The control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.
  • D. The control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

An administrator wants to check if the BlueMan service can access the DPU.
How can this be done?

  • A. Via a lightweight database operating in the DPU server
  • B. Via the DOCA Telemetry Service (DTS)
  • C. Via system logs
  • D. Via Linux dump files
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

A cloud engineer is looking to provision a virtual machine for machine learning using the NVIDIA Virtual Machine Image (VMI) and Rapids.
What technology stack will be set up for the development team automatically when the VMI is deployed?

  • A. Ubuntu Server, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver, Rapids
  • B. Ubuntu Server, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver
  • C. Cent OS, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI
  • D. Ubuntu Server, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

Which type of drift occurs when the relationship between input features and target variables changes over time, even if the input data distribution remains relatively stable?

  • A. Feature drift
  • B. Data drift
  • C. Concept drift
  • D. Label drift
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

Your organization is running multiple AI models on a single A100 GPU using MIG in a multi- tenant environment. One of the tenants reports a performance issue, but you notice that other tenants are unaffected.
What feature of MIG ensures that one tenant's workload does not impact others?

  • A. Automatic scaling of instances based on workload size.
  • B. Dynamic resource allocation based on workload demand.
  • C. Hardware-level isolation of memory, cache, and compute resources for each instance.
  • D. Shared memory access across all instances.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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