NVIDIA NCP-AIO VCE DOWNLOAD, EXAM NCP-AIO FORMAT

NVIDIA NCP-AIO Vce Download, Exam NCP-AIO Format

NVIDIA NCP-AIO Vce Download, Exam NCP-AIO Format

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NVIDIA AI Operations Sample Questions (Q233-Q238):

NEW QUESTION # 233
When deploying BCM using Helm, which Helm chart parameter controls the resource limits for the BCM pods (CPU and Memory)?

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

Answer: B

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 # 234
You need to deploy a containerized AI application from NGC using a CI/CD pipeline. The pipeline should automatically build, test, and deploy the container image to a Kubernetes cluster whenever changes are pushed to the code repository. Which of the following CI/CD tools and practices are most suitable for this scenario?

  • A. Use Jenkins with the Docker plugin to build the image and 'kubectl apply' to deploy it.
  • B. Employ CircleCl with a custom script to build the image and directly push it to the container registry.
  • C. Utilize GitLab CI/CD with kaniko to build the image and Helm to manage deployments.
  • D. Integrate NVIDIA GPU Cloud (NGC) CLI into the CIICD pipeline to fetch and deploy pre-built containers.
  • E. Use AWS CodePipeline with AWS CodeBuild to build the image and AWS Elastic Kubernetes Service (EKS) to deploy the application.

Answer: C,D,E

Explanation:
B, D and E are the correct. GitLab CI/CD with kaniko and Helm provides a robust and scalable solution for building and deploying container images. NGC CLI allows fetching and deploying pre-built containers, simplifying the process. AWS CodePipeline, CodeBuild, and EKS offer a complete CI/CD solution within the AWS ecosystem. Option A is a valid but less modern approach. Option C lacks a structured deployment process.


NEW QUESTION # 235
Which command-line tool is primarily used for creating and managing MIG instances on NVIDIA GPUs?

  • A. nvcc
  • B. nvidia-smi
  • C. nvidia-cuda-smi
  • D. nvidia-container-cli
  • E. nvprof

Answer: B

Explanation:
'nvidia-smi' is the primary command-line tool for managing and monitoring NVIDIA GPUs, including creating and managing MIG instances. The other options have different purposes related to CUDA development and containerization.


NEW QUESTION # 236
After installing Kubernetes on your NVIDIA hosts using BCM, you notice that the GPU metrics are not being collected by your monitoring system (e.g., Prometheus). You've confirmed that the NVIDIA Device Plugin is running correctly and GPUs are accessible to containers.
What is the next MOST likely component to investigate and how would you address it?

  • A. The Kubernetes API server is throttling metrics requests. Increase the API server's throttling limits for metrics requests.
  • B. The NVIDIA Data Center GPU Manager (DCGM) exporter is not deployed or configured correctly. Deploy and configure the DCGM exporter to expose GPU metrics in a Prometheus-compatible format.
  • C. The Prometheus service discovery is not configured to scrape metrics from the NVIDIA Device Plugin endpoint. Update the Prometheus configuration to include the device plugin's metrics endpoint.
  • D. The kubelet's resource usage metrics endpoint is not properly configured. Edit the kubelet configuration file to enable GPU metrics collection.
  • E. The cluster's logging driver is interfering with metrics collection. Switch to a different logging driver (e.g., journald) that doesn't conflict with metrics collection.

Answer: B

Explanation:
The NVIDIA Data Center GPU Manager (DCGM) exporter is specifically designed to collect and expose GPU metrics in a format that Prometheus can consume. If GPU metrics are not being collected, the DCGM exporter is the most likely culprit. The other options are less directly related to GPU metric collection. Option A pertains more to core Kubernetes metrics, option C relates to generic prometheus service discovery which isn't specialized to GPU data. Logging drivers and API throttling are less likely to directly block metrics collection.


NEW QUESTION # 237
Consider an HPC application heavily reliant on CODA. You plan to leverage MIG to optimize GPU resource allocation within your cluster.
Which configuration approach would BEST ensure the HPC application benefits from high GPU compute capability while coexisting with other workloads?

  • A. Create MIG instances tailored to the HPC application's specific memory and compute needs, allocating the necessary resources without over-provisioning. Utilize the remaining resources for other workloads.
  • B. Disable MIG and allow the HPC application to utilize the entire GPU for maximum performance.
  • C. Create multiple small MIG instances and distribute the HPC workload across them.
  • D. Create a single, large MIG instance dedicated solely to the HPC application, maximizing its compute capacity.
  • E. Configure all MIG instances with equal memory and compute allocation to provide a fair distribution of resources.

Answer: A

Explanation:
Tailoring MIG instances to the HPC application's specific requirements ensures efficient resource allocation and allows other workloads to utilize the remaining GPU capacity. D is not ideal for concurrent workloads. A and E don't account for specific workload requirements.


NEW QUESTION # 238
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