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NVIDIA NCP-AII Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA Certified Professional – AI Infrastructure
Exam Number:NCP-AII
Real Exam Qty:50
Exam Duration:90 minutes
Certificate Validity Period:2 years
Related Certifications:NCP-DES
NCP-AI
Exam Price:$195 USD
Passing Score:700 (scale of 0-1000)
Exam Format:Multiple Select, Multiple Choice
Available Languages:English
Sample Questions:NVIDIA NCP-AII Sample Questions
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:Recommended: hands-on experience with NVIDIA AI infrastructure products; basic knowledge of Linux, networking, and data center operations
Official Syllabus URL:https://www.nvidia.com/en-us/certifications/ncp-ai-infra/

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NVIDIA provides the most reliable and authentic NVIDIA NCP-AII Exam prep material there is. The 3 kinds of NVIDIA NCP-AII Preparation formats ensure that there are no lacking points in a student when he attempts the actual NCP-AII exam.

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
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3
  • 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 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.
Topic 5
  • 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.

NVIDIA AI Infrastructure Sample Questions (Q185-Q190):

NEW QUESTION # 185
You are deploying a multi-tenant AI infrastructure where different users or groups have isolated network environments using VXLAN. Which of the following is the MOST important consideration when configuring the VTEPs (VXLAN Tunnel Endpoints) on the hosts to ensure proper network isolation and performance?

Answer: B

Explanation:
The most critical aspect of VXLAN configuration for multi-tenancy is ensuring that each tenant has a unique VNI. The VNI is a 24-bit identifier that segments the VXLAN network into isolated logical networks. Using unique VNIs guarantees that traffic from one tenant remains isolated from other tenants, preventing data leakage and ensuring security. Using overlapping or shared VNIs would defeat the purpose of VXLAN- based isolation.


NEW QUESTION # 186
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 # 187
A system administrator needs to install a GPU/DPU in a server. The server has a free PCI-e slot, there are enough free PCI-e lanes, and there is enough room for the card. Which procedure should be followed?

Answer: D

Explanation:
Installing a GPU or DPU requires confirming sufficient power, verifying that the required cables are compatible with the server platform, powering the server down before removing or connecting hardware, and wearing an ESD bracelet to prevent electrostatic damage to the card.


NEW QUESTION # 188
An AI training cluster with NVIDIA GPUs experiences prolonged data loading times during checkpoint reloading, causing GPUs to idle frequently, CPU utilization during data transfers remains high. Which solution most effectively optimizes storage-to-GPU throughput while reducing CPU overhead?

Answer: D

Explanation:
GPUDirect Storage enables data to move more directly between storage and GPU memory, bypassing much of the CPU-mediated data path. This reduces CPU overhead during checkpoint reloads and improves storage-to-GPU throughput, helping keep GPUs fed instead of idle.


NEW QUESTION # 189
A system engineer needs to set the vGPU scheduling behavior for all GPUs to share the scheduling equally with the default time slice length. What command should be used?

Answer: C

Explanation:
The RmPVMRL=0x00 setting configures the NVIDIA vGPU scheduler to use equal-share scheduling with the default time slice length. The esxcli system module parameters set -m nvidia command is the correct ESXi command format for setting NVIDIA kernel module parameters globally for the GPUs.


NEW QUESTION # 190
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