NVIDIA NCP-AIO study guide
NVIDIA NCP-AIO · Work through your exam, one topic at a time.
The NVIDIA Certified Professional – AI Operations (NCP-AIO) is a professional credential for engineers who build, operate, and keep AI infrastructure healthy - the people running GPU clusters, AI factories, and the software stacks on top of them. This independent study page is not affiliated with, endorsed by, or sponsored by NVIDIA; it is written for candidates who want a clear, honest map of the exam before they book it. The NVIDIA NCP-AIO study guide has 5 sections and about 1,333 words.
01AI Infrastructure and Data Center Operations
This domain tests the physical and operational layer of an NVIDIA AI cluster: rack power, cooling, out-of-band management, GPU health telemetry, and fault escalation. Questions often ask you to choose a rack design or interpret a sensor or XID reading.
298 words, about 1 min02AI Factory Deployment and Management
This domain covers turning bare metal into a validated, multi-tenant AI factory: reference architecture, provisioning, firmware baselines, scheduling, and acceptance testing. Base Command Manager, Kubernetes device plugins, and schedulers such as Run:ai are common context.
255 words, about 1 min03Accelerated Computing and GPU Operations
This domain tests GPU architecture and day-two operations: SM and Tensor Core utilization, memory bandwidth, MIG, NVLink topology, persistence mode, clocks, ECC, XID events, and DCGM metrics. Match the workload to the bottleneck: Tensor Core math, memory bandwidth, or interconnect.
259 words, about 1 min04AI Networking and Interconnect
This domain covers the fabrics that feed GPUs: NVLink/NVSwitch inside a node, InfiniBand or RoCE between nodes, rail-optimized topologies, GPUDirect RDMA, and congestion control. Keep GPU east-west traffic on the high-speed fabric; use management Ethernet only for control.
267 words, about 1 min05AI Software Stack and Workload Operations
This domain tests the container-to-cluster software path: NVIDIA driver, CUDA toolkit, cuDNN/NCCL, NGC containers, Kubernetes device plugin, Triton Inference Server, and job observability. The host driver must be compatible with the container's CUDA runtime; the NVIDIA Container Toolkit exposes GPUs to containers.
254 words, about 1 min