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NVIDIA NCP-AII Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA Certified Professional – AI Infrastructure
Exam Number:NCP-AII
Related Certifications:NCP-DES
NCP-AI
Exam Format:Multiple Select, Multiple Choice
Certificate Validity Period:2 years
Exam Duration:90 minutes
Available Languages:English
Passing Score:700 (scale of 0-1000)
Exam Price:$195 USD
Real Exam Qty:50
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 NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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 5
  • 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.

NVIDIA AI Infrastructure Sample Questions (Q139-Q144):

NEW QUESTION # 139
ClusterKit's NCCL bandwidth test shows 350 GB/s on a 400G InfiniBand fabric. How should this result be interpreted?

Answer: B

Explanation:
Interpreting NCCL (NVIDIA Collective Communications Library) results requires understanding the difference between theoretical link rate and effective bus bandwidth. A 400G (NDR) InfiniBand link has a theoretical unidirectional capacity of 50 GB/s. In an 8-GPU DGX system, where each GPU is typically mapped to its own 400G HCA, the aggregate theoretical bandwidth is 400 GB/s. However, NCCL performance benchmarks reportBus Bandwidth, which accounts for the mathematical overhead of collective operations like all_reduce. Achieving350 GB/s(Option A) is widely considered optimal and representative of a healthy, well-configured fabric. This indicates that the system is effectively utilizing GPUDirect RDMA to bypass host memory and that the network switches are routing traffic without significant congestion or packet drops. Expecting >390 GB/s (Option C) is unrealistic once protocol headers and algorithmic overhead are subtracted from the raw bit rate. Rerunning with CPU stress (Option D) would not provide insights into the InfiniBand/GPU fabric health.


NEW QUESTION # 140
An engineer needs to completely remove NVIDIA GPU drivers from an Ubuntu 22.04 system to troubleshoot conflicts. Which command sequence ensures all driver components are purged?

Answer: A

Explanation:
Purging nvidia-* removes NVIDIA driver packages and related configuration files, while autoremove cleans up unused dependencies and kernel module packages left behind. This provides a more complete cleanup than removing only a single driver package.


NEW QUESTION # 141
After ClusterKit reports "GPU-Host latency exceeds threshold," which NVIDIA diagnostic tool should be used to isolate hardware faults?

Answer: C

Explanation:
"GPU-Host latency" issues in NVIDIA DGX or HGX systems are frequently caused by incorrect PCIe affinity or sub-optimal NUMA (Non-Uniform Memory Access) mapping. If a GPU is forced to communicate with a CPU core or an HCA that is not on its local PCIe switch/root complex, latency increases significantly as data must cross the QPI/UPI inter-processor links. The command nvidia-smi topo -m provides a detailed matrix of the system's internal topology, showing how GPUs, CPUs, and NICs are connected. It identifies whether the connection is via a single PCIe switch (PIX), multiple switches (PXB), or across the CPU (SYS).
By inspecting this map, an administrator can identify if a software process is pinned to the wrong NUMA node or if a hardware path is unexpectedly degraded. While DCGM (Option C) is good for checking component health, it doesn't map the logical-to-physical affinity paths that cause specific latency "threshold" warnings.


NEW QUESTION # 142
An InfiniBand fabric is experiencing intermittent packet loss between two high-performance compute nodes. You suspect a faulty cable or connector. Besides physically inspecting the cables, what software-based tools or techniques can you employ to diagnose potential link errors contributing to this packet loss?

Answer: C

Explanation:
All of the mentioned options are valid techniques for diagnosing link errors. 'ibdiagnet' performs a thorough fabric analysis. Monitoring switch port counters reveals link-level errors. 'iperf/ibperf identifies packet loss, which can be correlated with switch error counters. A comprehensive approach combining these methods is most effective.


NEW QUESTION # 143
When configuring an out-of-core (OOC) 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:
HPL_OOC_SAFE_SIZE reserves a safety margin of GPU memory for drivers, runtime overhead, and other allocations during out-of-core HPL execution. Setting it prevents the OOC run from consuming all available GPU memory and helps avoid out-of-memory failures during large matrix burn-in tests.


NEW QUESTION # 144
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