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NVIDIA NCP-AII 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
トピック 2	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
トピック 3	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
トピック 4	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.
トピック 5	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.

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NVIDIA NCP-AII対応受験、NCP-AII PDF

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NVIDIA AI Infrastructure 認定 NCP-AII 試験問題 (Q182-Q187):

質問 # 182
You are an infrastructure engineer tasked with validating a new AI training cluster before releasing it to users.
Your team wants to perform a NeMo burn-in to ensure both hardware and software are reliable and ready for production workloads. Which of the following actions are required as part of a proper NeMo burn-in process?
Pick the 2 correct responses below.

A. Configure a representative NeMo training or pretraining recipe and set up an executor to launch the job across intended nodes and GPUs.
B. Test inference using the NeMo API and approve the environment if the model outputs valid predictions.
C. Run the configured NeMo training job repeatedly or for an extended duration, monitoring for errors, stalls, or performance drops across all GPUs and nodes.
D. Download a pre-trained NeMo model and use it for a quick accuracy check on a user dataset, then consider the burn-in complete if results are reasonable.

正解：A、C

解説：
A proper NeMo burn-in should use a representative training or pretraining workload and run it for a sustained period across the intended GPUs and nodes. The goal is not only to prove that NeMo can import or that a model can return an inference result. The goal is to exercise the full AI infrastructure path: GPUs, CPU memory, CUDA, NCCL, network fabric, storage, container runtime, scheduler integration, and framework stack. Configuring a representative NeMo recipe and executor ensures the job launches with realistic distributed-training behavior and uses the same type of resources expected in production. Running the job repeatedly or for an extended duration helps expose intermittent issues such as NCCL stalls, GPU Xid errors, storage slowdowns, thermal throttling, node instability, or performance regression. A quick accuracy check on a pre-trained model is not a burn-in because it does not adequately stress distributed training. Inference-only testing is also insufficient for validating a training cluster. NeMo burn-in is valuable because it validates both the software framework and the underlying NVIDIA AI infrastructure under realistic workload pressure.

質問 # 183
An infrastructure engineer in an AI factory has successfully replaced a power supply unit on an NVIDIA DGX H100. After installation, both the IN and OUT LEDs on the new power supply illuminate solid green. Which NVSM CLI command should the engineer use to quickly verify the overall system status and ensure it is operating as expected?

A. nvsm show power
B. nvsm show powermode
C. nvsm show health
D. nvsm show alerts

正解：C

解説：
nvsm show health provides a quick overall health summary for the DGX system after hardware replacement. It is the appropriate command to confirm that the system is operating normally and that no health issues remain after the PSU installation.

質問 # 184
You are configuring a server with multiple GPUs for CUDA-aware MPI. Which environment variable is critical for ensuring proper GPU affinity, so that each MPI process uses the correct GPU?

A. CUDA DEVICE ORDER
B. CUDA VISIBLE DEVICES
C. CUDA LAUNCH BLOCKING-I
D. LD LIBRARY PATH
E. MPI GPU SUPPORT

正解：B

解説：
'CUDA VISIBLE DEVICES' is essential for GPU affinity. It allows you to specify which GPUs are visible to a particular process. Without it, all processes might try to use the same GPU, leading to performance bottlenecks. controls the order in which GPUs are enumerated. specifies the path to shared libraries. is hypothetical. forces synchronous CUDA calls.

質問 # 185
You are expanding a DGX-based deep learning cluster to train on large, high-resolution images that cannot fit into local cache. Multiple nodes will access this data concurrently and require high performance. Which storage and networking solution best meets these requirements?

A. Deploy a high-performance parallel filesystem across InfiniBand or 40/100GbE, ensuring at least
3GB/s per node and scalable aggregate bandwidth for all cluster workloads.
B. Recommend general-purpose object storage for all training data as it's optimized for deep learning workloads and distributed data access at any scale
C. Increase the SSD RAID-0 local cache size in each node so it can absorb most training data, making network storage type and speed less important for performance.
D. Implement a standard NFS server on a 10GbE network since the cluster can access the export and job performance will not be impacted.

正解：A

解説：
Large high-resolution datasets that cannot fit in local cache require shared storage that can sustain concurrent access from many DGX nodes. A high-performance parallel filesystem over InfiniBand or high-speed Ethernet provides the per-node throughput and scalable aggregate bandwidth needed to keep distributed deep learning workloads fed with data.

質問 # 186
A customer has just completed the first boot of their DGX system and is prompted to create an administrative user. What is the correct approach for setting up this user to ensure secure BMC and GRUB access?

A. Skip the creation of a new user and retain the default admin account for BMC and GRUB access.
B. Create a unique, strong, lower-case username and password that will be used for both BMC and GRUB access, avoiding default or weak credentials.
C. Use "sysadmin" as the username and a simple password for ease of management.
D. Create separate usernames for BMC and GRUB to maximize flexibility.

正解：B

質問 # 187
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