NVIDIA NCP-AII study guide
NVIDIA NCP-AII · Work through your exam, one topic at a time.
The NVIDIA Certified Professional - AI Infrastructure (NCP-AII) is a professional credential aimed at engineers who build, operate, and troubleshoot GPU-accelerated data-center environments. It suits data-center and HPC engineers, AI platform and MLOps infrastructure staff, systems administrators moving into accelerated computing, and field or support engineers who deploy NVIDIA-based clusters for internal or customer workloads. The NVIDIA NCP-AII study guide has 8 sections and about 2,894 words.
01System and Server Hardware Installation
Turning a bare chassis into a GPU server the software stack can see and trust: PCIe slot and lane planning, SXM/HGX baseboard handling, power delivery, airflow, and the firmware settings that decide whether accelerators enumerate at all. Enumerate before you diagnose software.
381 words, about 2 min02GPU and Accelerator Configuration
Making a healthy GPU behave predictably: persistence mode, ECC policy, MIG partitioning, NVLink state, and clock and power limits. The exam expects command-level familiarity with nvidia-smi and its MIG and NVLink subcommands.
365 words, about 2 min03Networking for AI Clusters (InfiniBand and Ethernet)
Whether you can design, verify, and debug the fabric that carries collective communication. Expect rail-optimized topology, subnet manager operation, RoCEv2 lossless configuration, NCCL transport selection, and link-level counters.
347 words, about 2 min04Storage Configuration and Management
Feeding accelerators fast enough to keep them busy: local NVMe scratch layout, parallel filesystem clients, GPUDirect Storage, checkpoint strategy, and the bandwidth arithmetic that tells you whether the pipeline can keep up. Do the bandwidth math first.
367 words, about 2 min05Software Stack Installation and Management (drivers, CUDA, containers)
The install order and version contracts that make a GPU node usable: kernel driver, CUDA toolkit, container runtime integration, Fabric Manager, and DCGM, plus the compatibility rules that tie them together. OS and kernel headers, firmware/BIOS, GPU driver (DKMS or precompiled), CUDA toolkit, container toolkit, then Fabric Manager and DCGM.
376 words, about 2 min06Cluster Orchestration and Workload Management
Scheduling GPUs without oversubscribing them: Slurm GRES and MIG resource types, Kubernetes device plugin and GPU Operator behavior, topology-aware placement, and how slicing modes change what a pod or job can request. GPUs are consumed through GRES.
328 words, about 2 min07Monitoring, Validation, and Troubleshooting
Separating a real hardware fault from a configuration problem, quickly and with evidence. Expect DCGM diagnostics, Xid interpretation, ECC and retired-page inspection, log collection, and burn-in methodology.
363 words, about 2 min08Security, Reliability, and Operational Best Practices
Running an AI cluster that stays up and stays contained: secure boot and module signing, multi-tenant isolation, firmware baselines, out-of-band management, redundancy, and change management for driver and fabric upgrades. Secure boot requires signed modules.
367 words, about 2 min