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最新的 NVIDIA-Certified Professional NCP-AII 免費考試真題:

1. Consider the following 'Ispci' output snippet after installing an NVIDIA GPU:
03:00.0 VGA compatible controller: NVIDIA Corporation Device 2236 (rev al) Subsystem: Dell Device 1234 Kernel driver in use: nvidia Kernel modules: nvidiafb, nouveau, nvidia drm, nvidia What does this output indicate, and what is the POTENTIAL issue?

A) The GPU is installed, but the open-source 'nouveau' driver is loaded instead of the proprietary NVIDIA driver, which could lead to suboptimal performance.
B) The GPU is installed, but the 'nvidiafb' driver is causing conflicts. Solution: Remove the 'nvidiafb' module.
C) The GPU is not properly installed, as indicated by 'Device 2236'. Solution: Re-seat the GPU.
D) The GPU is correctly installed, and the proprietary NVIDIA driver is loaded.
E) Solution: Blacklist 'nouveau' and ensure the NVIDIA driver is correctly configured.
F) The output indicates a hardware failure and requires a replacement of the GPU.

2. You are tasked with validating a newly installed NVIDIAAIOO Tensor Core GPU within a server. You need to confirm the GPU is correctly recognized and functioning at its expected performance level. Describe the process, including commands and tools, to verify the following aspects: 1) GPU presence and basic information, 2) PCle bandwidth and link speed, and 3) Sustained computational performance under load.

A) 1) Use 'nvidia-smi' for presence and basic info. 2) Use 'nvlink-monitor' for bandwidth/speed. 3) Run a CPU-bound benchmark to avoid GPU bottlenecks.
B) 1) Check BIOS settings for GPU detection. 2) Use 'Ispci -vv' to check PCle speed. 3) Run a PyTorch ImageNet training script.
C) 1) Use 'nvidia-smi' for presence and basic info. 2) PCle speed is irrelevant. 3) Run the 'nvprof profiler during a CUDA application.
D) 1) Use 'nvidia-smi' for presence and basic info. 2) Use 'nvidia-smi -q -d pcie' for bandwidth/speed. 3) Run a CUDA-based matrix multiplication benchmark (e.g., using cuBLAS) with increasing matrix sizes and monitor performance.
E) 1) Use 'Ispci I grep NVIDIA' for presence, 'nvidia-smi' for basic info. 2) Use 'nvidia-smi -q -d pcie' for bandwidth/speed. 3) Run a TensorFlow ResNet50 benchmark.

3. You have configured MIG on your A100 GPU, creating several MIG instances. You now want to allocate a specific MIG instance to a Docker container. How would you specify the necessary device option when running the 'docker run' command to ensure the container uses only that MIG instance? Assuming the MIG instance UUID is GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

A) docker run -gpus device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...
B) docker run -runtime=nvidia --device=GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ...
C) docker run -device=/dev/nvidia0 .
D) docker run -gpus all .
E) docker run -nvidia-visible-devices GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

4. You are using Docker Compose to define a multi-container application that includes a GPU-accelerated service. How would you configure the service in the 'docker-compose.ymr file to leverage the NVIDIA runtime?

A) Set the environment variable 'NVIDIA VISIBLE in the service definition.
B) Add 'deploy: resources: reservations: devices: - driver: nvidia count: all capabilities: [gpu]' to the service definition.
C) Add 'nvidia: all' to the service definition.
D) Add 'command: -gpus all' to the service definition.
E) Add 'runtime: nvidia' to the service definition.

5. You are configuring a switch port connected to a host in an NCP-AII environment. The host is running RoCEv2. To optimize performance and prevent packet loss, which flow control mechanism should you enable on the switch port?

A) Priority Flow Control (PFC) or 802.1 Qbb, specifically for the traffic class associated with RoCEv2.
B) TCP flow control.
C) None; flow control is not needed with RoCEv2.
D) Simple Network Management Protocol (SNMP).
E) Spanning Tree Protocol (STP).

問題與答案：