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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Configuration | 25-30% | - System installation and setup - Cluster configuration - Software deployment - Network configuration |
| Topic 2: AI Infrastructure Fundamentals | 15-20% | - NVIDIA software stack overview - GPU architecture basics - AI and Deep Learning concepts - Data center infrastructure requirements |
| Topic 3: Monitoring and Management | 15-20% | - Resource utilization - Performance monitoring - NVIDIA management tools - Troubleshooting basics |
| Topic 4: Security and Best Practices | 10-15% | - Compliance considerations - Operational best practices - Security fundamentals |
| Topic 5: NVIDIA AI Infrastructure Components | 25-30% | - NVIDIA networking solutions (Mellanox) - Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA DGX systems |
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185. Frage
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?
Antwort: D
Begründung:
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).
186. Frage
After ClusterKit reports "GPU-Host latency exceeds threshold," which NVIDIA diagnostic tool should be used to isolate hardware faults?
Antwort: B
Begründung:
"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.
187. Frage
A financial services firm is deploying an AI model for fraud detection that requires rapid inference and data retrieval across multiple sites. Which feature should their storage system prioritize?
Antwort: C
Begründung:
Fraud detection inference across multiple sites needs fast access to data and flexibility for different workloads and applications. Multi-protocol storage with low latency supports rapid retrieval, real-time inference pipelines, and distributed access patterns required for time-sensitive AI fraud detection.
188. Frage
An AI cluster needs to transmit data at 200Gbps over a distance of 2km using single-mode fiben Considering cost and performance, which transceiver type is the most appropriate?
Antwort: D
Begründung:
200GBASE-LR4 is the most appropriate. 'LR' designates Long Reach, typically up to 10km on single-mode fiber. SR4 is short reach (typically copper or very short fiber). ER4 is Extended Reach (up to 40km). CR4 is copper, for very short distances. DR4 is typically used for up to 500m to 2km distances depending on the specific implementation, so while possible LR4 is a better fit for guaranteed 2km.
189. Frage
You are preparing a GPU cluster for distributed AI training. Before running workloads, you need to validate the cluster's hardware. Which of the following steps is the most effective?
Antwort: B
Begründung:
Single-node GPU benchmarks validate that each node's GPUs are functioning correctly and delivering expected performance before distributed training begins. This establishes the hardware baseline needed before moving on to multi-node communication and workload testing.
190. Frage
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