FREE SAMPLE NVIDIA NCP-AIO QUESTIONS & VCE NCP-AIO EXAM SIMULATOR

Free Sample NVIDIA NCP-AIO Questions & VCE NCP-AIO Exam Simulator

Free Sample NVIDIA NCP-AIO Questions & VCE NCP-AIO Exam Simulator

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Tags: Free Sample NCP-AIO Questions, VCE NCP-AIO Exam Simulator, NCP-AIO Latest Training, NCP-AIO New Exam Camp, NCP-AIO Relevant Questions

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NVIDIA AI Operations Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are attempting to run a Docker container that leverages NVIDIA GPUs, but encounter the following error: 'docker: Error response from daemon: could not select device driver "nvidia" with capabilities: [[gpu]].' What is the most probable cause and how would you resolve it?

  • A. The NVIDIA Container Toolkit is not correctly installed or configured. Verify the installation and configuration following NVIDIA's documentation.
  • B. The Docker daemon is not configured to use the NVIDIA runtime as its default runtime. Set the default runtime by editing '/etc/docker/daemon.json' and restarting the Docker daemon.
  • C. The '-gpus alr flag is missing from the 'docker run' command. Include the flag to enable GPU access for the container.
  • D. The NVIDIA driver version is incompatible with the Docker daemon. Update the NVIDIA drivers to the latest version.
  • E. The host system does not have any NVIDIA GPUs installed. Verify that NVIDIA GPUs are installed and detected by the system.

Answer: A,B

Explanation:
The error message 'could not select device driver nvidia with capabilities: [[gpu]]' points directly to a problem with the NVIDIA Container Toolkit (A), and incorrect NVIDIA runtime setup and configuration within the Docker daemon. Verify installation of NVIDIA Container Toolkit, and set the default runtime in 'letc/docker/daemon.json' file.


NEW QUESTION # 65
You're deploying an AI inference application using NVIDIA Triton Inference Server in a Kubernetes cluster. Which of the following storage options is MOST suitable for storing the trained models, considering scalability and access speed?

  • A. Kubernetes EmptyDir volume.
  • B. Local SSD drives directly attached to the Kubernetes nodes.
  • C. HostPath volume on each node.
  • D. Network File System (NFS) share mounted directly to each pod.
  • E. Kubernetes PersistentVolume backed by a cloud object storage service (e.g., AWS S3, Google Cloud Storage).

Answer: E

Explanation:
Using a cloud object storage service via PersistentVolume provides scalability, durability, and accessibility across the Kubernetes cluster. NFS can be a bottleneck, HostPath isn't portable, and EmptyDir is ephemeral. Local SSDs can be fast, but more difficult to manage and scale within Kubernetes for a shared model repository.


NEW QUESTION # 66
A user reports that their AI training job running on a BCM-managed cluster is experiencing slow 1/0 performance. What steps would you take to diagnose and resolve the issue, considering the potential involvement of storage?

  • A. Monitor the storage system's performance metrics (IOPS, latency, throughput) using the storage vendor's monitoring tools.
  • B. Inspect the pod's logs for any I/O related errors or warnings.
  • C. Increase the pod's CPU and memory limits to improve 1/0 performance.
  • D. Check the network bandwidth between the compute nodes and the storage system using *iperf or similar tools.
  • E. Verify that the correct storage class is being used for the persistent volumes used by the training job.

Answer: A,B,D,E

Explanation:
Network bandwidth can be a bottleneck. Storage system metrics are crucial for identifying storage-related issues. The storage class determines the underlying storage type and performance characteristics. Pod logs might contain error messages. Increasing CPU/memory won't directly solve I/O performance issues if the bottleneck is elsewhere.


NEW QUESTION # 67
What should an administrator check if GPU-to-GPU communication is slow in a distributed system using Magnum IO?

  • A. Limit the number of GPUs used in the system to reduce congestion.
  • B. Increase the system's RAM capacity to improve communication speed.
  • C. Disable InfiniBand to reduce network complexity.
  • D. Verify the configuration of NCCL or NVSHMEM.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Slow GPU-to-GPU communication in distributed systems often relates to theconfiguration of communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM.
Ensuring these libraries are properly configured and optimized is critical for efficient GPU communication.
Limiting GPUs or increasing RAM does not directly improve communication speed, and disabling InfiniBand would degrade performance.


NEW QUESTION # 68
Consider the following code snippet using NVSHMEM:
If this program hangs indefinitely after 'nvshmem_barrier all()' inside the 'if (my_pe 0)' block, what is the MOST likely cause?

  • A. Deadlock due to insufficient NVLink bandwidth.
  • B. PE 0 is waiting for other PEs to complete within the loop, but other PEs have already finished and finalized.
  • C. Incorrect use of 'CUDA VISIBLE DEVICES'
  • D. Memory corruption in 'nvshmem_malloc' .
  • E. Incorrect use of 'MPl Barrier'.

Answer: B

Explanation:
The issue is a classic synchronization problem. PE 0 is the only PE entering the loop, and it calls to get the values from other PEs. However, the other PEs have already proceeded past the first , executed , and potentially even before PE 0 attempts to read their values. Thus, PE 0 is waiting for PEs that are no longer in a state to respond. MPI_Barrier isn't the issue, nor is NVLink bandwidth. Memory corruption or CUDA_VISIBLE_DEVICES issues would likely cause crashes, not hangs at this specific point.


NEW QUESTION # 69
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