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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 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 AI Infrastructure Sample Questions (Q16-Q21):

NEW QUESTION # 16
When configuring an out-of-core HPL burn-in for a 40B matrix on 8x H100 nodes, which environment variable prevents GPU out-of-memory errors while reserving space for drivers?

Answer: D

Explanation:
The correct option is export HPL_OOC_SAFE_SIZE=4.0. NVIDIA HPL out-of-core mode allows matrix data that exceeds GPU memory capacity to be placed in host memory, but the GPU still needs reserved memory for drivers and runtime overhead. NVIDIA documents HPL_OOC_SAFE_SIZE as the amount of GPU memory, in GiB, reserved for the driver and not used by HPL out-of-core mode; increasing it is recommended when GPU out-of-memory errors occur. HPL_OOC_MODE=0 disables out-of-core mode, which would not help run a larger 40B matrix. HPL_OOC_NUM_STREAMS=8 changes the number of CUDA streams used for out-of-core operations, but it does not reserve driver memory.
HPL_OOC_MAX_GPU_MEM=90 limits total GPU memory use, but the specific variable intended to leave safe driver space is HPL_OOC_SAFE_SIZE. During cluster burn-in, this setting helps preserve test validity while avoiding false failures caused by memory reservation issues rather than actual hardware instability.


NEW QUESTION # 17
You are preparing a GPU cluster for distributed AI training. Before running workloads, you need to validate the cluster's hardware. Which of the following steps is the most effective?

Answer: D

Explanation:
Single-node GPU benchmarks validate that each node's GPUs are functioning correctly and delivering expected performance before distributed training begins. This establishes the hardware baseline needed before moving on to multi-node communication and workload testing.


NEW QUESTION # 18
You are following the official steps to install the NVIDIA Container Toolkit using a package manager on Ubuntu. After importing the NVIDIA package repository and GPG key, what is the next action?

Answer: D

Explanation:
The NVIDIA Container Toolkit (formerly nvidia-docker2) is the essential middleware that allows Docker, Podman, or Containerd to "see" and utilize the host's GPU hardware. The standard installation workflow on Debian-based systems like Ubuntu involves three core phases: repository configuration, package installation, and runtime configuration. Once the GPG key is added (to ensure package integrity) and the .list file is placed in /etc/apt/sources.list.d/ (to point to the NVIDIA production servers), the local package index must be refreshed via apt-get update. Immediately following this, the administrator must install the toolkit using the command sudo apt-get install -y nvidia-container-toolkit. Rebooting (Option A) is unnecessary at this stage because no kernel modules have been modified yet. Downloading the CUDA Toolkit (Option D) is a separate step; notably, the Container Toolkit allows containers to run CUDA applications even if the host only has the NVIDIA driver installed, making the driver-not the host CUDA toolkit-the primary prerequisite.


NEW QUESTION # 19
You are deploying a BlueField-3 DPU within a secure environment. You are required to enable secure boot to prevent unauthorized firmware from being loaded. Which steps are typically involved in enabling secure boot on a BlueField-3 DPU, starting from a factory- default state? (Select TWO)

Answer: B,C

Explanation:
The crucial steps are generating and enrolling cryptographic keys in the DPIJ's UEFI firmware, which establishes the chain of trust, and flashing the DPIJ with a secure boot-enabled OS image signed with those keys. Without these, secure boot won't function correctly. While updating the BMC firmware (E) is generally a good practice, and enabling IJEFI boot mode in the server's BIOS (C) is necessary for UEFI in general, they are not specific steps for enabling secure boot on the DPIJ itself . Installing host drivers (A) is also irrelevant to secure boot.


NEW QUESTION # 20
A machine learning platform stores multi-petabyte training datasets accessed concurrently by hundreds of GPUs. Administrators determine that the storage system must provide high aggregate throughput, parallel client access, and efficient scaling as additional storage nodes are added. Which storage solution is generally the most appropriate?

Answer: C

Explanation:
Parallel file systems such as BeeGFS and Lustre are designed for high-performance computing and AI environments. They distribute metadata and data across multiple servers, enabling simultaneous high-bandwidth access by many clients. Single-server NFS implementations and local disks typically become bottlenecks as AI clusters grow in size and workload intensity.


NEW QUESTION # 21
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