최신 NVIDIA-Certified Professional NCP-AII 무료샘플문제:
1. After replacing a faulty NVIDIA GPU, the system boots, and 'nvidia-smi' detects the new card. However, when you run a CUDA program, it fails with the error "'no CUDA-capable device is detected'". You've confirmed the correct drivers are installed and the GPU is properly seated. What's the most probable cause of this issue?
A) The GPIJ is not properly initialized by the system due to a missing or incorrect ACPI configuration.
B) The new GPU is incompatible with the existing system BIOS.
C) The 'LD LIBRARY PATH* environment variable is not set correctly.
D) The user running the CUDA program does not have the necessary permissions to access the GPU.
E) The CUDA toolkit is not properly configured to use the new GPU.
2. Your AI infrastructure includes several NVIDIAAI 00 GPUs. You notice that the GPU memory bandwidth reported by 'nvidia-smi' is significantly lower than the theoretical maximum for all GPUs. System RAM is plentiful and not being heavily utilized. What are TWO potential bottlenecks that could be causing this performance issue?
A) The GPUs are connected via PCle Gen3 instead of PCle Gen4.
B) The CPU is using older DDR4 memory with low bandwidth
C) Insufficient CPU cores assigned to the training process.
D) Inefficient data loading from storage to GPU memory.
E) The NVIDIA drivers are not configured to enable peer-to-peer memory access between GPUs.
3. You are working with a BlueField-3 DPIJ and wish to programmatically control the PCle link speed and width. Which interface exposes the most direct way to manage these low-level hardware settings on the DPU?
A) The standard Linux ethtoor utility.
B) Using the Mellanox mlxconfig utility or its equivalent.
C) Modifying the device tree blob (DTB) and rebooting the DPIJ.
D) Directly accessing the PCle configuration space via '/sys/bus/pci/..:.
E) The NVIDIA Management Library (NVML).
4. Which of the following storage technologies are most suitable for storing large training datasets used in deep learning, considering both performance and cost?
A) Tape backup systems
B) High-performance NVMe SSDs in a local RAID configuration
C) A parallel file system (e.g., BeeGFS, Lustre) deployed on NVMe SSDs
D) SATA HDDs in a network-attached storage (NAS) configuration
E) Object storage (e.g., AWS S3, Azure Blob Storage) accessed directly from the training nodes
5. You're deploying a large language model for inference using NVIDIA Triton Inference Server. You need to validate that the server can handle the expected query load while maintaining acceptable latency. Which tools and metrics are most relevant for this validation?
A) All of the above
B) Check CPU utilization using 'top'
C) Utilize Triton's built-in metrics endpoint to track latency, throughput, and request queue length.
D) Employ a load testing tool (e.g., 'locust' , 'JMeter') to simulate client requests and measure response times.
E) Use 'nvidia-smi' to monitor GPIJ utilization and memory usage.
질문과 대답:
| 질문 # 1 정답: A | 질문 # 2 정답: A,D | 질문 # 3 정답: B | 질문 # 4 정답: C | 질문 # 5 정답: A |
4 개 고객 리뷰
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