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NVIDIA NCP-AIO 考試概覽:

認證廠商:	NVIDIA
考試名稱:	NVIDIA 認證專業人士：AI 運營 (NCP-AIO)
考試代碼:	NCP-AIO
支援語言:	English
推薦課程:	NVIDIA 深度學習學院 (DLI) NVIDIA 訓練課程
考試報名:	NVIDIA 認證入口
範例考題:	NVIDIA NCP-AIO 範例考題
考試方式:	可能採用線上監考及/或授權測試中心交付方式 (NVIDIA 認證交付方式因地區和考試提供商而異)
官方大綱網址:	https://www.nvidia.com/en-us/training/certification/

NVIDIA NCP-AIO 考試大綱：

主題	簡介
主題 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.
主題 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.
主題 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.
主題 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.