NCP-AIO EXAM VALID DUMPS BOOK & AUTHORITATIVE NCP-AIO TEST PAPERS PASS SUCCESS

NCP-AIO Exam Valid Dumps Book & Authoritative NCP-AIO Test Papers Pass Success

NCP-AIO Exam Valid Dumps Book & Authoritative NCP-AIO Test Papers Pass Success

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NVIDIA AI Operations Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which of the following are benefits of using NVIDIA Fleet Command (Select all that apply)?

  • A. Simplified AI model deployment and management at the edge.
  • B. Automated over-the-air (OTA) updates for AI models and system software.
  • C. Enhanced security and access control for AI deployments.
  • D. Centralized monitoring and management of edge devices.
  • E. Automatic GPU driver updates for non-NVIDIA GPUs.

Answer: A,B,C,D

Explanation:
Fleet Command provides simplified deployment (A), centralized monitoring (B), OTA updates (C), and enhanced security (D). It does not provide driver updates for non-NVIDIA GPUs (E).


NEW QUESTION # 53
You're trying to build a Docker image for a PyTorch application that requires CUDA 11.6. Your Dockerfile includes the following line: 'FROM nvidia/cuda:ll .6-base-ubuntu20.04'. However, during the build process, you encounter errors related to missing CUDA libraries.
What could be the problem and what are possible solutions?

  • A. The 'nvidia/cuda:ll .6-base-ubuntu20.04' image only provides the base CUDA environment. You need to install additional CUDA libraries like 'libcudnn8' explicitly using 'apt-get' in your Dockerfile.
  • B. You are not setting the CUDA HOME environment variable inside the Dockerfile. Set 'ENV CUDA HOME=/usr/local/cuda'
  • C. You are using the wrong base image. Use 'nvidia/cuda:ll .6-devel-ubuntu20.04' instead, as it includes development libraries.
  • D. The Docker daemon does not have enough resources to build the image. Increase the Docker daemon's memory and CPU limits.
  • E. The host machine's NVIDIA driver version is incompatible with CUDA 11.6. Update the host driver to a compatible version.

Answer: A,C

Explanation:
The '-base image provides a minimal CUDA environment, often lacking necessary libraries. The '-dever image includes development libraries, and explicit installation of specific libraries (like cuDNN) might be required. CUDA_HOME might also be necessary to explicitly set, but the more likely issues are the missing libraries and the image selection.


NEW QUESTION # 54
A fleet of edge devices running AI inference applications experiences intermittent network connectivity. You need to configure Fleet Command to handle these disruptions gracefully. Which of the following actions should you take to ensure application resilience?

  • A. Implement a local caching mechanism on the edge devices to store inference results during network outages and synchronize them when connectivity is restored.
  • B. Configure Fleet Command to immediately roll back deployments when network connectivity is lost.
  • C. Instruct users to manually restart applications on the edge devices after network outages.
  • D. Disable all updates to the edge devices during periods of network instability.
  • E. Increase the timeout values for all Fleet Command operations.

Answer: A

Explanation:
A local caching mechanism allows edge devices to continue operating during network disruptions, ensuring application resilience. Rolling back deployments (A) is disruptive. Disabling updates (C) prevents improvements. Increasing timeouts (D) might help with transient issues but doesn't address the underlying problem. Manual restarts (E) are not scalable or reliable.


NEW QUESTION # 55
You've noticed consistently high GPU utilization but low overall throughput in your AI inference service. You suspect that a CUDA kernel is not efficiently utilizing the GPU's resources. Which profiling tool would provide the MOST detailed insights into kernel-level performance?

  • A. nvidia-smi'
  • B. DCGM
  • C. 'vmstat'
  • D. 'top'
  • E. NVIDIA Nsight Systems

Answer: E

Explanation:
NVIDIA Nsight Systems (and its successor Nsight Compute for kernel-level analysis) is specifically designed for profiling CUDA kernels. It provides detailed information on kernel execution time, memory access patterns, and instruction-level performance, allowing you to identify inefficiencies. 'nvidia-smr and DCGM provide high-level GPU monitoring, while 'top' and 'vmstat' are system-level tools.


NEW QUESTION # 56
When installing Kubernetes using BCM on NVIDIA servers, which of the following components are crucial for enabling GPU support within the cluster?

  • A. Containerd CRI
  • B. NVIDIA Container Runtime (nvidia-container-runtime)
  • C. kube-proxy running in IPVS mode
  • D. Kubernetes Device Plugin for NVIDIA GPUs
  • E. NVIDIA Driver on each worker node

Answer: B,D,E

Explanation:
The Kubernetes Device Plugin allows Kubernetes to discover and manage NVIDIA GPUs. The NVIDIA Container Runtime is a low-level library that provides the necessary hooks to expose the GPUs to containers. The NVIDIA driver is the foundation for all GPU operations. Kube-proxy mode and containerd CRI are important for general kubernetes networking and containerization but do not specifically enable GPU Support. IPVS is not specifically related and Containerd is not NVIDIA specific


NEW QUESTION # 57
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