NVIDIA NCP-AII SAMPLE QUESTIONS ANSWERS & CUSTOMIZED NCP-AII LAB SIMULATION

NVIDIA NCP-AII Sample Questions Answers & Customized NCP-AII Lab Simulation

NVIDIA NCP-AII Sample Questions Answers & Customized NCP-AII Lab Simulation

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Tags: NCP-AII Sample Questions Answers, Customized NCP-AII Lab Simulation, Authentic NCP-AII Exam Hub, Reliable NCP-AII Exam Pattern, NCP-AII Reliable Mock Test

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NVIDIA AI Infrastructure Sample Questions (Q264-Q269):

NEW QUESTION # 264
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. Check the system logs for firmware-related messages during boot.
  • C. Run 'ethtool -i on a network interface associated with the DPIJ.
  • D. Query the DPIJ's BMC (Baseboard Management Controller) via IPMI or Redfish.
  • E. Use the 'mst status' command to query the device status and firmware version.

Answer: E

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 # 265
A user reports that their CUDA application is running slower than expected after an NVIDIA driver update. You suspect a driver compatibility issue. How can you revert to a previous NVIDIA driver version on an Ubuntu system, assuming you have the older driver package 'nvidia-driver-470 470.82.00-0ubuntu1_amd64.deb'?

  • A. Simply install the .deb' package using 'sudo dpkg -i
  • B. Edit the '/etc/apt/sources.list' file to point to an older repository containing the desired driver version, then update and upgrade the system.
  • C. Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.
  • D. First, remove the current NVIDIA driver using 'sudo apt purge nvidia- s, then install the .deb' package using 'sudo dpkg -i nvidia-driver-470_470.82.00-Oubuntu1_amd64.deb' and resolve any dependency issues with 'sudo apt --fix-broken install'.
  • E. Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.

Answer: D

Explanation:
The safest and most reliable approach is to first remove the current NVIDIA driver using 'sudo apt purge nvidia- to avoid conflicts. Then, install the .deb' package using 'sudo dpkg -i' and resolve any potential dependency issues with 'sudo apt -fix-broken install'. 'nvidia-smi' cannot downgrade drivers. Editing "/etc/apt/sources.list' can be risky and lead to system instability. Directly installing with 'dpkg -i' without purging the old driver can cause conflicts. If you installed with .run' before, then you must uninstall using that method first.


NEW QUESTION # 266
After upgrading your NVIDIA drivers on a system with multiple GPUs, 'nvidia-smu reports 'No devices were found'. You've verified that the GPUs are physically connected correctly. What are the most likely causes and corresponding solutions?

  • A. The driver installation was interrupted or corrupted. Solution: Reinstall the driver, ensuring no errors during the process.
  • B. The NVIDIA driver is incompatible with the installed CUDA toolkit. Solution: Downgrade or upgrade the CUDA toolkit to match the driver's compatibility requirements.
  • C. The NVIDIA kernel modules failed to load. Solution: Rebuild the kernel modules using DKMS and reboot.
  • D. The X server is interfering with the driver. Solution: Stop the X server (e.g., 'sudo systemctl stop gdm3' or 'sudo systemctl stop lightdm') before running 'nvidia-smi'.
  • E. The user lacks necessary permissions. Solution: Add the user to the 'video' group.

Answer: A,C

Explanation:
The most common causes are failure to load the kernel modules, often due to upgrade issues requiring a DKMS rebuild and reboot, or a corrupted installation requiring reinstallation. User permissions and CUDA toolkit version are less common in this scenario where no devices are found. While stopping the X server can sometimes help, it's not the primary solution if 'nvidia-smri' can't find the GPUs at all.


NEW QUESTION # 267
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?

  • A. Replace the existing PSU with a higher wattage PSU that has at least four 8-pin PCIe power connectors.
  • B. Use a PCIe power splitter cable on one of the 8-pin connectors to power two GPUs.
  • C. Connect the GPUs using the motherboard's internal SATA power connectors.
  • D. Install only three GPUs and leave the fourth unpowered.
  • E. Underclock the GPUs significantly to reduce their power consumption below the available PSU capacity.

Answer: A

Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.


NEW QUESTION # 268
You are designing a network for a distributed training job utilizing multiple GPUs across multiple nodes. Which network characteristic is MOST critical for minimizing training time?

  • A. Low latency
  • B. High bandwidth
  • C. Large MTU
  • D. High packet loss rate
  • E. Low cost

Answer: A,B,C

Explanation:
Distributed training involves frequent data exchange between GPUs. High bandwidth is necessary to move large datasets quickly. Low latency reduces the round-trip time for communication. Large MTIJ (Maximum Transmission Unit) can reduce overhead by transmitting larger packets. High packet loss is detrimental to performance. Low cost should not be prioritized over performance in a distributed training environment.


NEW QUESTION # 269
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