VCE NCP-AIO FILES | RELIABLE NCP-AIO TEST ANSWERS

Vce NCP-AIO Files | Reliable NCP-AIO Test Answers

Vce NCP-AIO Files | Reliable NCP-AIO Test Answers

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NVIDIA AI Operations Sample Questions (Q267-Q272):

NEW QUESTION # 267
You are deploying a cloud VMI container with Kubernetes. Your application requires a specific NVIDIA driver version. How do you ensure the correct driver version is used within the container, especially when the host node might have a different driver version?

  • A. The NVIDIA driver version used on the host node is automatically inherited by the container; no specific configuration is needed.
  • B. Utilize the NVIDIA Device Plugin for Kubernetes and configure it to inject the correct driver libraries into the container during runtime.
  • C. Bake the required NVIDIA driver version directly into the container image during the Docker build process.
  • D. Specify the desired driver version in the Kubernetes Deployment manifest using the 'nvidia.com/gpu.driver.version' resource limit.
  • E. Configure the Kubernetes node's operating system to always use the desired NVIDIA driver version globally.

Answer: B,C

Explanation:
Using the NVIDIA Device Plugin allows for dynamic injection of driver libraries. Baking the driver into the container also works, but it results in a larger image and less flexibility. Option A is not a valid resource limit. Overriding the host OS driver is not practical for multi-tenant environments. Relying on host inheritance is risky as driver versions can vary.


NEW QUESTION # 268
You've implemented GPUDirect Storage in your data pipeline, but are seeing inconsistent performance gains. Sometimes it's significantly faster, other times it's barely better than your previous setup. Which of the following factors could explain this inconsistent behavior? SELECT TWO.

  • A. The CPU is still heavily involved in preprocessing the data after it's loaded into GPU memory.
  • B. The data being read is highly compressible.
  • C. The PCIe bandwidth between the NIC and GPU is saturated by other processes.
  • D. The CUDA compute version is not compatible with the version of GPUDirect Storage being used.
  • E. The storage system is experiencing variable latency.

Answer: A,E

Explanation:
Variable storage latency will directly impact the performance of GPUDirect Storage, as it relies on fast and consistent access to the storage devices. If the storage system is sometimes slow, the benefits of bypassing the CPU will be reduced. If the CPU is still heavily involved in preprocessing, it can become a bottleneck, limiting the overall performance gain from GPUDirect Storage. Data compressibility (A) and PCIe bandwidth (D) can affect performance in general, but don't necessarily explain the inconsistent nature of the performance gains. CUDA compute version (E) compatibility issues would likely lead to errors, not just inconsistent performance.


NEW QUESTION # 269
After updating the NVIDIA drivers on your NVSwitch-connected GPU server, 'nvsm' fails to start. The log file shows the following error: 'Failed to initialize NVML'. Which of the following actions is MOST likely to resolve the issue?

  • A. Disable SELinux.
  • B. Increase the allocated memory to the 'nvsm' process.
  • C. Ensure that the NVIDIA kernel modules are correctly loaded and that the CUDA toolkit is installed and configured properly.
  • D. Reinstall the operating system.
  • E. Downgrade to the previous version of the NVIDIA drivers.

Answer: C

Explanation:
NVML (NVIDIA Management Library) is a core component required for 'nvsm' to function. If NVML fails to initialize, it usually indicates a problem with the NVIDIA drivers, kernel modules, or CUDA installation. Verifying these components is the most direct way to resolve the issue. Downgrading can work but first you should verify your installation.


NEW QUESTION # 270
You are using NVSHMEM to manage shared memory across multiple GPUs in a multi-node cluster. Your application is crashing with out- of-memory errors, even though the reported GPU memory usage is well below the total available. You have already confirmed sufficient physical RAM on all nodes. What is the MOST likely cause, related to NVSHMEM configuration, of these out-of-memory errors?

  • A. The 'NVSHMEM SYMMETRIC SIZE environment variable is set too low.
  • B. The NCCL library is not properly initialized.
  • C. 'CUDA_VISIBLE DEVICES is set incorrectly
  • D. The InfiniBand drivers are outdated.
  • E. The CUDA driver version is incompatible with NVSHMEM.

Answer: A

Explanation:
The 'NVSHMEM SYMMETRIC SIZE environment variable defines the total amount of shared memory available to NVSHMEM across all nodes. If this value is too small, even if individual GPUs have sufficient memory, the overall NVSHMEM shared memory pool may be exhausted, leading to out-of-memory errors. CUDA driver incompatibility, NCCL issues, and outdated InfiniBand drivers could cause other problems, but they are less likely to directly cause out-of-memory errors when individual GPU usage is low . CUDA_VISIBLE_DEVICES may effect device enumeration and memory allocation, but is the master environment variable.


NEW QUESTION # 271
When installing Kubernetes using BCM on NVIDIA hosts, what is the purpose of the 'nvidia-container-toolkit' and how does it interact with the container runtime (e.g., Docker or containerd)?

  • A. It handles network configuration for containers running on NVIDIA hosts. It configures the container network interface (CNI) to optimize network performance for GPU-accelerated workloads.
  • B. It's a set of libraries and utilities that allow the container runtime to isolate and expose GPU devices to containers. It intercepts container creation requests and configures the container to access the GPU.
  • C. It's a command-line tool for building and deploying GPU-enabled container images. It automates the process of adding the NVIDIA driver to container images.
  • D. It's a tool for monitoring GPU utilization within containers. It directly queries the NVIDIA driver for GPU metrics and exposes them via a REST API. It does not interact directly with the container runtime.
  • E. It's a Kubernetes operator that automatically installs and manages the NVIDIA driver on worker nodes. It replaces the need for manual driver installation.

Answer: B

Explanation:
The 'nvidia-container-toolkit' is the bridge between the container runtime (Docker, containerd) and the NVIDIA driver. It allows the container runtime to correctly configure containers to use the GPUs on the host. It achieves this by intercepting container creation requests and modifying the container's configuration to enable GPU access.


NEW QUESTION # 272
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