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NEW QUESTION # 134
An infrastructure engineer is preparing a new AI cluster for production use, which relies on NVIDIA switches and high-speed optical transceivers for node connectivity. The team is finalizing network validation before launching large-scale training jobs. Why is it critical to confirm and align the firmware version on all switch transceivers prior to production?
Answer: C
Explanation:
Matching supported transceiver firmware versions across the cluster helps ensure link stability, expected bandwidth, and compatibility between NVIDIA switches and optical modules. This reduces the risk of link flaps, degraded throughput, and intermittent fabric issues during large- scale AI training workloads.
NEW QUESTION # 135
You've successfully deployed BlueField OS to your SmartNlC. You need to verify that the Mellanox Ethernet driver (mlx5) is loaded and functioning correctly. What command would you use to confirm this?
Answer: B
Explanation:
The 'Ismod' command lists loaded kernel modules. 'grep mlx5 filters the output to show if the Mellanox Ethernet driver is loaded. 'Ispci' lists PCl devices, 'ifconfig' shows network interfaces but not necessarily the driver, and 'ethtool' displays driver information for a specific interface.
NEW QUESTION # 136
You are installing multiple NVIDIA GPUs in a server for a deep learning cluster. To optimally utilize the GPUs, which software component(s) are MANDATORY after the physical installation and driver setup? (Select TWO)
Answer: A,D
Explanation:
The NVIDIA CUDA Toolkit provides the necessary libraries and tools for GPU-accelerated computing. A deep learning framework (TensorFlow or PyTorch) is required to build and train deep learning models that leverage the GPUs. While a web browser and text editor might be useful, they are not mandatory. Spreadsheet applications have no purpose here.
NEW QUESTION # 137
An engineer needs to completely remove NVIDIA GPU drivers from an Ubuntu 22.04 system to troubleshoot conflicts. Which command sequence ensures all driver components are purged?
Answer: D
Explanation:
The correct command sequence is sudo apt-get purge nvidia-* & & sudo apt-get autoremove. Purging removes NVIDIA driver packages and their configuration files, while autoremove removes dependency packages that were installed automatically and are no longer required. NVIDIA's current data center driver removal guidance for Ubuntu 22.04 and 24.04 uses apt remove --autoremove --purge with NVIDIA-related package patterns, including driver, Fabric Manager, firmware, headless, DKMS, compute, decode, encode, and related libraries. A simple apt-get remove nvidia-driver-550 removes only one package and may leave DKMS modules, libraries, configuration files, Fabric Manager packages, or version-specific dependencies behind. Manually deleting /usr/lib/nvidia is unsafe because it bypasses package management and can leave the system in an inconsistent state. ubuntu-drivers uninstall is not the most complete answer for fully purging all NVIDIA driver components during conflict troubleshooting. After removal, administrators should verify loaded modules, package state, and reboot before installing the intended validated driver stack. Clean removal is important in AI infrastructure because driver conflicts can break CUDA, NCCL, DCGM, Fabric Manager, and container runtime behavior.
NEW QUESTION # 138
An engineer needs to validate NVLink Switch functionality on a DGX H100 system with 8 GPUs. Which NCCL command verifies intra-node NVLink bandwidth?
Answer: C
Explanation:
The NVIDIA Collective Communications Library (NCCL) "Tests" are used to verify the maximum achievable bandwidth of the interconnects. On a DGX H100, the GPUs are connected via a dedicated high- bandwidthNVLink Switch fabric(NVLink 4), which provides significantly higher throughput than PCIe. To validate the intra-node (within a single server) performance, the all_reduce_perf test is used. The command in Option D is specifically designed to stress all 8 GPUs (-g 8) across a wide range of message sizes (8 bytes to
16G). The use of the environment variable NCCL_TESTS_SPLIT with the bitwise "OR" or "AND" masks allows the engineer to isolate specific traffic patterns or groups of GPUs to ensure the NVLink switches are distributing the load evenly. For a standard 8-GPU H100 tray, achieving a "Bus Bandwidth" of ~450 GB/s to
900 GB/s (depending on the precision and message size) confirms that the NVLink fabric is operating at its theoretical peak. Using only 4 GPUs (Option B) or 1 GPU (Option C) would not provide a complete picture of the NVLink switch bisection bandwidth.
NEW QUESTION # 139
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