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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
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
  • 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 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.

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NVIDIA AI Infrastructure Sample Questions (Q27-Q32):

NEW QUESTION # 27
A system administrator needs to use DCGM-exporter to export GPU metrics to an external system to create interactive dashboards. What system can the DCGM-exporter use without additional configuration?

Answer: D

Explanation:
DCGM-exporter exposes NVIDIA GPU telemetry in a Prometheus-compatible metrics format by default. Prometheus can scrape these metrics directly, and the collected data can then be visualized in dashboard tools such as Grafana.


NEW QUESTION # 28
You are setting up a multi-node A1 cluster with NVIDIA GPUs and InfiniBand for inter-node communication. You need to ensure the InfiniBand network is functioning optimally for GPU-accelerated workloads. What steps would you take to validate the InfiniBand installation and performance?

Answer: C

Explanation:
Sibstat' verifies interface status. 'ibping' and 'ibperf are InfiniBand-specific tools for latency and bandwidth testing. NCCL (NVIDIA Collective Communications Library) is critical for distributed training, and provides valuable diagnostic information. The other options are either incomplete or rely on tools not specific to InfiniBand.


NEW QUESTION # 29
After ClusterKit reports " GPU-Host latency exceeds threshold, " which NVIDIA diagnostic tool should be used to isolate hardware faults?

Answer: B

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 # 30
You are configuring a RoCEv2 (RDMA over Converged Ethernet) network using BlueField-2 DPUs. You are observing packet loss and performance degradation. You suspect that Congestion Control is not working correctly. What configuration parameter most directly impacts RoCEv2 congestion control behavior?

Answer: E

Explanation:
ECN is the key mechanism for RoCEv2 congestion control. It allows network devices to signal congestion to the endpoints, which can then reduce their transmission rate. Proper ECN configuration on both the switches and the DPIJ interfaces is essential for effective congestion control. While PFC can prevent packet loss due to buffer overflow, it doesn't address congestion in the same way as ECN. The other options are less directly related to RoCEv2 congestion control.


NEW QUESTION # 31
An AI operations team investigates unexpectedly slow distributed training. GPU utilization averages only 55%, while storage, CPU, and InfiniBand monitoring all indicate normal performance. DCGM reports no hardware faults, but profiling shows GPUs frequently waiting during synchronization phases. Which area should the engineers investigate first?

Answer: A

Explanation:
When GPU utilization drops primarily during synchronization despite healthy hardware and storage performance, inefficient collective communication is a likely cause. NCCL configuration-- including transport selection, topology detection, and network optimization--should be examined first. Storage firmware, CPU thermals, and MIG configuration are less likely to produce synchronization-specific delays in this scenario.


NEW QUESTION # 32
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