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NVIDIA AI Infrastructure Sample Questions (Q230-Q235):

NEW QUESTION # 230
You're managing a cluster of servers with BlueField-2 DPUs. One server is experiencing intermittent network connectivity issues. You suspect a problem with the DPU's firmware. Which of the following is the MOST reliable method to determine the CURRENT firmware version of the BlueField-2 DPIJ?

A. Examine the '/proc/driver/mlx4_core/versiorf file.
B. Use the 'mst status' command to query the device status and firmware version.
C. Check the system logs for firmware-related messages during boot.
D. Run 'ethtool -i on a network interface associated with the DPIJ.
E. Query the DPIJ's BMC (Baseboard Management Controller) via IPMI or Redfish.

Answer: B

Explanation:
'mst status' is the most reliable method. This command, part of the Mellanox OFED tools, is specifically designed to query the status of Mellanox devices, including the DPLJ, and accurately reports the firmware version. While other methods might provide clues, 'mst statuS is the definitive source of truth.

NEW QUESTION # 231
You are troubleshooting performance issues in an A1 training clusten You suspect network congestion. Which of the following network monitoring tools would be MOST helpful in identifying the source of the congestion?

A. tcpdump/Wireshark
B. iPerf/Netperf
C. Ping
D. Traceroute
E. netstat

Answer: A,B

Explanation:
iPerf/Netperf can measure network bandwidth and latency between nodes, allowing you to quantify congestion. tcpdump/Wireshark can capture and analyze network packets, providing detailed insights into traffic patterns and potential bottlenecks. Ping and Traceroute are useful for basic connectivity testing but not for detailed congestion analysis. netstat shows network statistics but doesn't capture packet-level details.

NEW QUESTION # 232
After physically installing a new NVIDIA GPU in a server, you boot the system. You notice that the GPU is not recognized by the operating system. You've verified the card is properly seated and powered. What are the MOST LIKELY causes and solutions? (Select TWO)

A. The motherboard BIOS/UEFI does not support the GPU. Solution: Update the motherboard BIOS/UEFI to the latest version.
B. The GPU is not compatible with the operating system. Solution: Reinstall the operating system.
C. The PCIe slot is faulty. Solution: Try installing the GPU in a different PCIe slot.
D. The GPU is defective. Solution: Return the GPU to the manufacturer.
E. The incorrect GPU drivers are installed or no drivers are installed at all. Solution: Download and install the latest drivers from the NVIDIA website.

Answer: A,E

Explanation:
The most common reasons for a GPU not being recognized are missing or incorrect drivers and an outdated BIOS/UEFI that doesn't support the card. A faulty PCIe slot is possible, but less likely as the initial troubleshooting step. Reinstalling the OS is rarely needed for driver issues. A defective GPU is possible but should be considered after other options are exhausted.

NEW QUESTION # 233
You are setting up a new A1 server with multiple NVIDIA GPUs for deep learning workloads. You need to install the NVIDIA Container Toolkit. After installing the necessary packages using your distribution's package manager, what is the most crucial next step to ensure proper integration with Docker?

A. Run 'nvidia-ctk runtime configure -runtime=docker' .
B. Manually create vetc/docker/daemon.json' and add 'nvidia' as a default runtime.
C. Run 'nvidia-smr to verify GPU detection. This automatically configures the container runtime.
D. Restart the Docker daemon (e.g., 'systemctl restart docker'
E. Reboot the server to load the new kernel modules.

Answer: A

Explanation:
The 'nvidia-ctk runtime configure -runtime=docker' command is essential to configure Docker to use the NVIDIA Container Runtime. This integrates the NVIDIA drivers and libraries within the Docker environment, allowing containers to access the GPUs. While restarting Docker is often helpful, 'nvidia-ctk' performs the necessary configuration steps. Rebooting is not always necessary after just installing a package, and manually editing 'daemon.json' is generally not the recommended approach. 'nvidia-smi' simply reports the status of the GPUs, it doesn't configure the runtime.

NEW QUESTION # 234
Which of the following are key benefits of using NVIDIA NVLink Switch in a multi-GPU server setup for AI and deep learning workloads?

A. Support for larger GPU memory pools than a single server can physically accommodate.
B. Reduced latency in inter-GPU data transfers.
C. Enhanced security features compared to PCle based interconnections.
D. Increased GPU-to-GPIJ communication bandwidth.
E. Simplified GPU resource management.

Answer: A,B,D

Explanation:
NVLink provides significantly higher bandwidth and lower latency compared to PCle, enabling faster communication between GPUs. NVLink switches allow for pooling of GPU memory across multiple servers, enabling training of larger models that wouldn't fit on a single server's GPU memory. While it enhances infrastructure capabilities, it doesn't inherently simplify GPU resource management or directly provide enhanced security compared to PCle regarding the data transfer. Management features might exist on top of the NVLink infrastructure.

NEW QUESTION # 235
......

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NVIDIA AI Infrastructure Sample Questions (Q230-Q235):

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Ray Morgan Ray Morgan

Hello world!

Biography

Valid Exam NCP-AII Braindumps, NCP-AII Exam Preparation

NCP-AII Exam Preparation | NCP-AII Test Review

NVIDIA AI Infrastructure Sample Questions (Q230-Q235):

2025 NCP-AII – 100% Free Valid Exam Braindumps | Authoritative NVIDIA AI Infrastructure Exam Preparation

Select Your Favourite
Category And Start Learning.