NCP-AIO VALID BRAINDUMPS PDF｜100% PASS｜LATEST QUESTIONS

NCP-AIO Valid Braindumps Pdf｜100% Pass｜Latest Questions

NCP-AIO Valid Braindumps Pdf｜100% Pass｜Latest Questions

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Tags: NCP-AIO Valid Braindumps Pdf, Reliable NCP-AIO Test Simulator, Test NCP-AIO Vce Free, NCP-AIO New Practice Materials, NCP-AIO Exam Quizzes

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NVIDIA AI Operations Sample Questions (Q278-Q283):

NEW QUESTION # 278
You are troubleshooting slow training times for a deep learning model. You suspect the storage is the bottleneck. You are using a network file system (NFS) to serve the dat a. Which of the following NFS mount options would most likely improve performance for read- heavy workloads?

  • A. Increasing the 'rsize' and 'wsize' to the maximum supported values by the NFS server and client.
  • B. Mounting the NFS share with the 'nolock' option.
  • C. Using the 'async' mount option.
  • D. Reducing the 'rsize' (read size) and 'wsize' (write size) to the smallest possible value.
  • E. Using the 'sync' mount option.

Answer: A,C

Explanation:
The 'async' mount option allows the client to write data to its cache and acknowledge the write before the data is actually written to the server, improving write performance. Increasing 'rsize' and 'wsize' allows for larger reads and writes, potentially improving throughput for large files commonly used in AI/ML datasets. However, exceeding the server's supported values will not help and may hurt performance.


NEW QUESTION # 279
When deploying BCM using Helm, which Helm chart parameter controls the resource limits for the BCM pods (CPU and Memory)?

  • A. 'resources.bcmLimits'
  • B. 'bcmResources.limits'
  • C. bcm.resources.limits'
  • D. 'deployment.resources.limits'
  • E. resources.limits'

Answer: E

Explanation:
The 'resources.limits' parameter in the Helm chart is the standard way to specify resource limits (CPU and memory) for pods deployed via Helm. This parameter is used to configure the resource requests and limits for the BCM pods.


NEW QUESTION # 280
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. Configure the Kubernetes node's operating system to always use the desired NVIDIA driver version globally.
  • B. Specify the desired driver version in the Kubernetes Deployment manifest using the 'nvidia.com/gpu.driver.version' resource limit.
  • C. The NVIDIA driver version used on the host node is automatically inherited by the container; no specific configuration is needed.
  • D. Utilize the NVIDIA Device Plugin for Kubernetes and configure it to inject the correct driver libraries into the container during runtime.
  • E. Bake the required NVIDIA driver version directly into the container image during the Docker build process.

Answer: D,E

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 # 281
A critical AI model inference application requires a specific version of the CUDA runtime. You deploy a containerized application using Fleet Command. How do you ensure the deployed container uses the correct CUDA version, minimizing conflicts with the host system?

  • A. Build a Docker image that includes the required CUDA runtime version and specify this image in the Fleet Command deployment manifest.
  • B. Install the required CUDA version directly on each edge device's host system.
  • C. Rely on Fleet Command to automatically install the correct CUDA version on each device.
  • D. Let the application install its own version of CUDA on each device during initialization.
  • E. Use the latest available CUDA drivers on the host system and hope for compatibility.

Answer: A

Explanation:
Containerization with a pre-defined CUDA version is the most reliable and isolated approach. Installing CUDA on the host (A) can lead to conflicts. Hoping for compatibility (B) is unreliable. Fleet Command doesn't automatically manage CUDA versions (D). Allowing the application to install CUDA (E) can cause system instability.


NEW QUESTION # 282
You're troubleshooting a pod that's failing to schedule on a node in your BCM-managed cluster, despite the node having available GPU resources. What's the most likely cause, and how would you diagnose it?

  • A. The Kubernetes scheduler is misconfigured. Restart the 'kube-scheduler* service on the master node.
  • B. The node is tainted, preventing pods from being scheduled. Use 'kubectl describe node to check for taints.
  • C. The pod's resource requests exceed the node's capacity. Use *kubectl describe pod to view resource requests and limits.
  • D. The node is cordoned, preventing new pods from being scheduled. Use 'kubectl get nodes' to check node status.
  • E. The pod is missing the necessary NVIDIA device plugin. Check the pod's YAML for 'nvidia.com/gpu' resource requests.

Answer: B,C,D,E

Explanation:
Taints prevent scheduling unless tolerated. Exceeding capacity prevents scheduling. The device plugin is required for GPU access. Cordoned nodes don't accept new pods. Restarting kube-scheduler is a less likely first step.


NEW QUESTION # 283
......

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