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NVIDIA NCP-AIO Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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.

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

NVIDIA AI Operations Sample Questions:

A GPU administrator needs to virtualize AI/ML training in an HGX environment.
How can the NVIDIA Fabric Manager be used to meet this demand?

  • A. GPU memory upgrade
  • B. Manage NVLink and NVSwitch resources
  • C. Video encoding acceleration
  • D. Enhance graphical rendering
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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What is the main benefit of using Kubernetes in AI operations when deploying machine learning models at scale in distributed environments?

  • A. Model training
  • B. Workflow orchestration
  • C. Container orchestration
  • D. Data labeling
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).

A system administrator needs to lower latency for an AI application by utilizing GPUDirect Storage.
What two (2) bottlenecks are avoided with this approach? (Choose two.)

  • A. PCIe
  • B. System Memory
  • C. NIC
  • D. CPU
  • E. DPU
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).

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

Correct Answer: B  🗳️

Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).

In AI operations, which tool or framework is typically used to package machine learning models along with their dependencies to ensure consistent execution across different computing environments?

  • A. Docker
  • B. Hadoop
  • C. Spark
  • D. Kubernetes
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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