NCP-AII Prüfungsguide: NVIDIA AI Infrastructure & NCP-AII echter Test & NCP-AII sicherlich-zu-bestehen

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NVIDIA NCP-AII Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Thema 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.
Thema 3
  • 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.
Thema 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.
Thema 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.

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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q108-Q113):

108. Frage
During East-West fabric validation on a 64-GPU cluster, an engineer runs all_reduce_perf and observes an algorithm bandwidth of 350 GB/s and bus bandwidth of 656 GB/s. What does this indicate about the fabric performance?

Antwort: C

Begründung:
An all_reduce_perf result showing around 350 GB/s algorithm bandwidth and 656 GB/s bus bandwidth indicates strong collective communication performance for a 64-GPU NDR InfiniBand fabric. The bus bandwidth being near the expected peak confirms the East-West fabric is performing optimally and GPUDirect RDMA paths are healthy.


109. Frage
A system administrator is virtualizing GPUs in VMware vSphere and needs to allocate a vGPU profile from the drop-down menu. There are several options available. What does "grid_a100-
20c" mean?

Antwort: C

Begründung:
grid_a100-20c identifies an NVIDIA A100 vGPU profile with a 20 GB framebuffer allocation. The profile name indicates the GPU family, assigned vGPU memory size, and compute-focused profile type used for VM GPU virtualization.


110. Frage
A system administrator receives an alert about a potential hardware fault on an NVIDIA DGX A100. The GPU performance seems degraded, and the system fans are operating loudly. What step should be recommended to identify and troubleshoot the hardware fault?

Antwort: D

Begründung:
When a DGX system exhibits high fan speeds and performance degradation, it is typically engaging in Thermal Throttling. High-performance GPUs like the A100 or H100 will automatically reduce their clock speeds (and thus performance) if they exceed safe temperature thresholds. The first and most critical diagnostic step is to run nvidia-smi. This utility provides immediate, real-time telemetry on GPU temperatures, power draw, and " Clocks Throttle Reasons. " By reviewing the output, an administrator can see if " Thermal " is listed as the reason for reduced clocks. This identifies whether the issue is environmental (blocked airflow/hot aisle temperature) or hardware-specific (a failed GPU thermal interface or a dead internal fan). Running more workloads (Option A) would exacerbate the heat, while a power drain (Option C) is a " last resort " that doesn ' t provide diagnostic data. nvidia-smi provides the evidentiary data needed to determine if an RMA (Return Merchandise Authorization) is required for the GPU tray.


111. Frage
You are troubleshooting a performance issue with a GPU-accelerated application running inside a Docker container. The 'nvidia-smi' output inside the container shows the GPU is being utilized, but the performance is significantly lower than expected. Which of the following could be the cause of this performance bottleneck?

Antwort: A,B,C,E

Begründung:
Several factors could contribute to reduced GPU performance within a Docker container, even if the GPU is being utilized. A heavily loaded CPU (A) can bottleneck data transfer to the GPU. Incompatible CUDA driver versions between host and container (C) cause unexpected errors, and CUDA drivers are important for GPU support. Frequent small memory transfers between CPU and GPU (D) can be inefficient. Overheating (E) can cause the GPU to throttle its performance. While shared memory optimization (B) can help, it's not always the primary cause of the initial performance drop.


112. Frage
A network architect deploys a Spectrum Ethernet fabric for a RoCE-based AI cluster. During stress testing, distributed training performance fluctuates significantly because packet loss occurs whenever multiple training jobs execute simultaneously. Which network feature should be prioritized to improve communication consistency?

Antwort: A

Begründung:
RoCE performs best on a properly configured lossless Ethernet network using technologies such as Priority Flow Control (PFC), Explicit Congestion Notification (ECN), and appropriate congestion management. Packet loss causes RDMA retransmissions and reduced performance.
GPU memory size and PCIe switches do not resolve network congestion issues.


113. Frage
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