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最新的 NVIDIA-Certified Professional NCP-AII 免費考試真題:

1. An NVIDIA-based AI server is experiencing intermittent network connectivity issues. You suspect a problem with the SFP28 transceiver connecting the server to the network. You want to use command-line tools to retrieve the DOM (Digital Optical Monitoring) information from the transceiver to diagnose the problem. Assuming you have appropriate network administration privileges, which command sequence is the MOST likely to provide the necessary DOM data (assuming 'eth0' is the interface)?

A)

B)

C)

D)

E)

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

3. You are troubleshooting a network performance issue in your NCP-AII environment. After running 'ibstat' on a host, you see the following output for one of the InfiniBand ports:

What does the 'LMC: 0' indicate, and what are the implications for network performance?

A) LMC : 0 indicates the port is operating at the lowest possible speed.
B) LMC : 0 is the default and expected value; it has no impact on performance.
C) LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.
D) LMC: 0 indicates that the link is down and not functioning correctly.
E) LMC : 0 indicates that the Subnet Manager is not running correctly.

4. You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a 'CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?

A) Set the environment variable inside the container to limit the number of GPUs visible to the application.
B) Monitor GPU memory usage both inside and outside the container using 'nvidia-smi' to identify memory leaks or excessive allocation.
C) Adjust the environment variable inside the container to ensure consistent GPU ordering.
D) Lower the compute capability using '-compute' parameter on docker run.
E) Increase the shared memory size for the container using the '-shm-size' flag when running the container.

5. After upgrading the NGC CLI using 'pip install -upgrade nvidia-cli' , some commands are no longer working as expected, producing errors related to missing modules. What is the most likely reason for this issue and how can you resolve it?

A) The NGC CLI upgrade introduced breaking changes. Review the NGC CLI release notes and update your scripts accordingly.
B) The system's PATH variable has not been updated to reflect the new NGC CLI installation location. Update the PATH variable accordingly.
C) The upgrade process might have corrupted the NGC CLI installation. Reinstall the package using 'pip install -force-reinstall nvidia-cli'.
D) The Python environment used by the NGC CLI might be broken or inconsistent. Create a new virtual environment and reinstall the NGC CLI in the new environment.
E) The host's operating system must be re-imaged.

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