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Valid NCP-AII Exam Dumps Materials - NCP-AII Quiz Cram - PDF4Test

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NVIDIA AI Infrastructure Sample Questions (Q35-Q40):

NEW QUESTION # 35
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. Edit the '/etc/apt/sources.list' file to point to an older repository containing the desired driver version, then update and upgrade the system.
B. 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'.
C. Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.
D. Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.
E. Simply install the .deb' package using 'sudo dpkg -i

Answer: B

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 # 36
An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue?

A. Upgrade the GPUs to models with larger memory capacity.
B. Disable other models running on the same GPU to free up memory.
C. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently.
D. Reduce the batch size and concurrency of the offending model in the Triton configuration.
E. Increase the system RAM to accommodate the growing memory footprint.

Answer: C,D

Explanation:
Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn't address the core problem of the memory leak in the first place.

NEW QUESTION # 37
You need to uninstall all NVIDIA drivers and associated packages from a Linux system cleanly. Which command sequence is the most reliable for achieving this after stopping the display manager (e.g., 'sudo systemctl stop gdm3')?

A. Running the .run' installer with the '-uninstall' option (if the driver was installed this way)
B. 'sudo apt purge nvidia- (on Debian/Ubuntu-based systems)
C. Deleting the Vusr/lib/nvidia and 'lusr/share/nvidia directories.
D. 'sudo yum remove nvidia- (on RHEL/CentOS-based systems)
E. 'sudo dnf remove nvidia- (on Fedora-based systems)

Answer: A,B,D,E

Explanation:
The appropriate package manager command (like 'apt purge' , 'yum remove' , or 'dnf remove' with the 'nvidia- wildcard) is the most reliable for removing driver packages. If the driver was installed using the ' .'run' installer, running it with '-uninstall' is also effective. Directly deleting directories is not recommended as it may leave behind configuration files and dependencies. Always stop the display manager before uninstalling to avoid conflicts.

NEW QUESTION # 38
During NVLink Switch configuration, you encounter issues where certain GPUs are not being recognized by the system. Which of the following troubleshooting steps are most likely to resolve this problem?

A. Ensure that the NVLink Switch firmware is compatible with the installed GPUs.
B. Check the Power supply for enough capacity and stability.
C. Verify that all NVLink cables are securely connected and properly seated.
D. Reinstall the operating system.
E. Check the system BIOS settings to ensure that NVLink is enabled and configured correctly.

Answer: A,C,E

Explanation:
Explanation:Physical connection issues (A), BIOS configuration (B), and firmware incompatibility (C) are the most common causes of GPUs not being recognized. Reinstalling the operating system is a drastic measure that is unlikely to solve the problem. Checking the Power supply may also required to ensure the complete system have enough capacity and stability.

NEW QUESTION # 39
You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough. How can you reduce the load on the CPU and improve the overall training throughput?

A. Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GPU.
B. Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.
C. Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.
D. Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.
E. Move the training data to a local NVMe drive on the training node.

Answer: A

Explanation:
Using NVIDIA DALI (option C) allows you to offload data augmentation and preprocessing tasks from the CPU to the GPU, freeing up CPU resources for other tasks and enabling faster data loading. Moving to a local NVMe drive (A) bypasses BeeGFS but doesn't address the CPU bottleneck. Increasing MDSs (B) improves metadata performance but doesn't directly help with the CPU-bound data augmentation. Decreasing the batch size (D) reduces the workload but doesn't solve the underlying CPU bottleneck. Data compression (E) can increase CPU load due to the decompression process.

NEW QUESTION # 40
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