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100% Pass Quiz 2025 NVIDIA NCP-AII: NVIDIA AI Infrastructure–The Best Upgrade Dumps

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NVIDIA AI Infrastructure Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which of the following statements are correct regarding the use of NVIDIA GPUs with Docker containers?

A. The 'nvidia-smr command can only be run on the host machine, not inside a Docker container.
B. The NVIDIA Container Toolkit allows you to run GPU-accelerated applications in Docker containers without modifying the container image.
C. Using environment variables like 'CUDA VISIBLE DEVICES' within the container can influence which GPUs are accessible to the application.
D. CUDA libraries are required inside the container if your application uses CODA.
E. You must install NVIDIA drivers inside the Docker container to enable GPU support.

Answer: B,C,D

Explanation:
The NVIDIA Container Toolkit allows GPU-accelerated apps to run in Docker without altering the image. The host's drivers are leveraged. CUDA libraries are necessary inside the container if your app uses CUDA. is used to control GPU visibility within the container. Drivers are not needed inside the container because they're managed by the host (making B incorrect), and 'nvidia-smi' can be run inside containers if the NVIDIA Container Toolkit is properly set up (making C incorrect).

NEW QUESTION # 98
You have a BlueField-2 DPU running Ubuntu. After upgrading the MLNX OFED drivers, the DPU fails to boot properly. You are presented with a GRUB prompt. Which sequence of commands is most likely to help you boot into a working kernel?

Answer: E

Explanation:
When a boot fails after a driver upgrade, it's often because the new kernel/initrd is faulty. The most likely solution is to boot into the previous , known-good kernel. Option C attempts this by specifying '-old' in the kernel and initrd filenames, attempting to load the older, hopefully functional, kernel. It assumes the root partition is /dev/sda2 but is booting an older kernel. Option A uses '$(uname -r)' which likely resolves to the problematic kernel that caused the failure. Option B lacks version information and may also load a broken kernel. Option D uses /dev/nvme0n1p2, which might be incorrect for the root partition on the BlueField. Option E will try to load grub normally, which might fail again.

NEW QUESTION # 99
A developer reports that their CUDA application running on a MIG instance is experiencing significantly reduced memory bandwidth compared to running on a full GPU. What are the potential causes for this performance bottleneck? (Select all that apply)

A. The CUDA driver version is not compatible with the MIG configuration, resulting in reduced performance.
B. The application is not optimized to take advantage of the MIG instance's specific memory bandwidth limitations.
C. MIG instances inherently provide higher memory bandwidth due to their partitioned nature, so this report must be incorrect.
D. The application is exceeding the memory capacity of the MIG instance, leading to excessive swapping.
E. The MIG instance has a smaller memory allocation compared to the full GPIJ, thus limiting the application's memory footprint.

Answer: A,B,D,E

Explanation:
MIG instances have smaller memory allocations (A) compared to the full GPU, which naturally limits memory footprint. Applications may not be optimized for MIG's bandwidth limitations (B) and might require tuning. Exceeding memory capacity will trigger swapping (C), significantly reducing performance. Incompatible CUDA drivers (D) can lead to performance degradation. MIG instances don't inherently offer higher bandwidth (E); they divide the overall GPU resources.

NEW QUESTION # 100
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 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.
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 host's operating system must be re-imaged.
D. The NGC CLI upgrade introduced breaking changes. Review the NGC CLI release notes and update your scripts accordingly.
E. The upgrade process might have corrupted the NGC CLI installation. Reinstall the package using 'pip install -force-reinstall nvidia-cli'.

Answer: A,D

Explanation:
Breaking changes in the NGC CLI upgrade (B) are a possibility, requiring script updates. An inconsistent Python environment (C) can also cause issues after an upgrade. Reinstalling (A) or updating the PATH (D) might not resolve the issue if the environment itself is the problem. OS re-imaging is highly unnecessary (E).

NEW QUESTION # 101
You're experiencing unexpected memory errors during the execution of a large-scale deep learning training job. Suspecting faulty memory modules, which diagnostic tool is best suited to perform a comprehensive memory test on the server's RAM?

A. DCGM
B. memtest86+
C. nvprof
D. Ishw
E. nvidia-smi

Answer: B

Explanation:
'memtest86+' is specifically designed for performing thorough memory tests on system RAM, identifying potential errors and hardware faults. 'nvidia-smi' is for GPU monitoring, 'nvprof is a profiler, DCGM can test GPU memory, but not system RAM specifically. 'Ishw' lists hardware information, but doesn't perform diagnostic tests.

NEW QUESTION # 102
......

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