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NEW QUESTION # 64
An administrator notices that a server is not collecting telemetry data such as traffic flows, performance faults, and events. In which network does this information flow?
Answer: D
NEW QUESTION # 65
You are deploying a multi-GPU server for deep learning training. After installing the GPUs, the system boots, but 'nvidia-smi' only detects one GPU. The motherboard has multiple PCle slots, all of which are physically capable of supporting GPUs. What is the most probable cause?
Answer: D
Explanation:
Incorrect BIOS/UEFI settings are the most likely cause when GPUs are physically present but not detected. The BIOS controls PCle lane allocation and slot enabling. Reseating GPUs is a good first step, but if the BIOS is misconfigured, it won't resolve the issue. Insufficient power is also a possibility, but BIOS configuration is more common in initial setup.
NEW QUESTION # 66
What is the primary function of the NVIDIA Container Toolkit, and how does it facilitate the use of GPUs within containerized environments? (Multiple Answers)
Answer: A,E
Explanation:
The NVIDIA Container Toolkit allows containers to access and utilize NVIDIA GPUs by injecting the necessary drivers and libraries into the container runtime environment and It enables monitoring of GPU utilization within containers. While it requires proper drivers to be installed, the toolkit does not manage host drivers directly. The NVIDIA container toolkit relies on container runtimes, and container runtimes manage the container lifecycle. The container toolkit does not automatically install drivers inside containers.
NEW QUESTION # 67
An organization deploys a Kubernetes AI platform using GPU Operator. Several GPU nodes appear in the cluster, but containers requesting GPU resources remain in a Pending state. The administrator verifies that NVIDIA drivers are correctly installed. Which missing component is the most likely cause?
Answer: C
Explanation:
The NVIDIA Device Plugin advertises GPU resources to Kubernetes so that the scheduler can allocate GPUs to pods. Without it, GPUs remain invisible to Kubernetes even if drivers are installed correctly. DCGM Exporter provides monitoring, Node Feature Discovery adds hardware labels, and CUDA Toolkit inside containers does not expose GPU resources.
NEW QUESTION # 68
Refer to the output:
~ $ sudo nvsm show healthinfo
-Timestamp: Sat Dec 16 16:26:32 2017 -0800
Version: 17.12-5
Checks-BIOS Revision [5.11].........................
DGX Serial Number [YSY72800016)..................
Verify installed DIMM memory sticks........................Healthy
...[output truncated)
Verify Ethernet controllers...........................Healthy
Verify installed GPU's..............................Unhealthy
Checking output of 'lspci' for expected GPU's
Missing GPU at PCI address '07:00.0'
Verify installed InfiniBand controllers....................Healthy
Verify PCIe switches..................................Healthy
...[output truncated)
What insights can a system administrator gain regarding the DGX system's health?
Answer: D
Explanation:
The output provided is a result of theNVIDIA System Management (NVSM)tool, specifically the nvsm show healthinfo command. NVSM is an essential diagnostic framework for NVIDIA DGX systems that monitors hardware health, identifies faults, and helps ensure the system remains within its validated operational state.
In this specific diagnostic trace, the system reports that the "Verify installed GPU's" check has returned a status ofUnhealthy. To provide a root cause, NVSM cross-references the live hardware enumeration from the lspci command against the system's known "Golden Configuration" (the hardware manifest defined in the firmware). The explicit error message,"Missing GPU at PCI address '07:00.0'", indicates that the system expects a GPU module to be present at that specific PCIe bus address, but the hardware is not responding or visible to the bus.
This insight allows a system administrator to conclude that aGPU is missingfrom the logical perspective of the system. This is a critical hardware fault rather than a software or driver issue. In a DGX H100 or A100 system, this could be caused by a physical module failure, a power delivery issue to that specific segment of the GPU baseboard, or a failure in the PCIe switch fabric. Because the DGX relies on a full set of 8 GPUs for high-speed collective communications (NCCL), a single missing GPU will prevent the node from participating in large-scale training jobs, requiring physical inspection or a GPU tray replacement (RMA).
NEW QUESTION # 69
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