ASSOCIATE NCP-AIO LEVEL EXAM & NCP-AIO VCE TORRENT

Associate NCP-AIO Level Exam & NCP-AIO Vce Torrent

Associate NCP-AIO Level Exam & NCP-AIO Vce Torrent

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NVIDIA AI Operations Sample Questions (Q158-Q163):

NEW QUESTION # 158
Which configuration file dictates the initial settings and parameters for the Base Command Manager (BCM) installation?

  • A. bcm.conf
  • B. base_command.config
  • C. bcm_config.yaml
  • D. nvidia bcm.ini
  • E. cluster_config.json

Answer: C

Explanation:
The 'bcm_config.yaml' file is the primary configuration file used during the initial installation and setup of Base Command Manager (BCM). It specifies various parameters such as the database connection details, authentication methods, and other system-level settings.


NEW QUESTION # 159
The 'nvsm' service is consistently crashing on one of your nodes. Analyzing the core dump reveals a segmentation fault related to memory access within the NVSwitch driver. What is the MOST appropriate course of action?

  • A. Increase the swap space on the node.
  • B. Disable the NVSwitch on the affected node.
  • C. Report the issue to NVIDIA support with the core dump and relevant system information.
  • D. Recompile the NVSwitch driver with debugging symbols.
  • E. Try different versions of CUDA.

Answer: C

Explanation:
Segmentation faults related to driver code usually indicate a bug within the driver itself. Reporting the issue with a core dump allows NVIDIA engineers to investigate and provide a fix. Trying to debug the driver yourself (recompiling) or disabling the NVSwitch are less effective solutions for this type of problem. Different versions of CUDA also can cause problems, but first report with the core dump.


NEW QUESTION # 160
You are using BCM to manage a large cluster of GPU servers. You want to implement a mechanism to automatically scale the number of BCM instances based on the load. What Kubernetes feature would be MOST suitable for this purpose?

  • A. Cluster Autoscaler
  • B. Horizontal Pod Autoscaler (HPA)
  • C. kube-scheduler
  • D. Vertical Pod Autoscaler (VPA)
  • E. Node Auto-Provisioning

Answer: B

Explanation:
The Horizontal Pod Autoscaler (HPA) is the most suitable Kubernetes feature for automatically scaling the number of BCM instances (pods) based on resource utilization (e.g., CPU, memory). HPA monitors the resource usage of the BCM pods and automatically adjusts the number of replicas to maintain the desired resource levels. VPA adjusts the resource requests and limits of individual pods. Cluster Autoscaler adds or removes nodes from the cluster. Node Auto-Provisioning is related to node management. Kube-scheduler schedules pods onto nodes.


NEW QUESTION # 161
You are managing a fleet of edge devices using NVIDIA Fleet Command. After deploying a new AI model, you observe that the model is consuming excessive resources on several devices, leading to performance degradation. What steps can you take within Fleet Command to address this issue?

  • A. Redeploy the same model to see if the issue resolves itself.
  • B. Remotely reboot the affected edge devices.
  • C. Use Fleet Command's monitoring tools to identify the specific resources being overutilized and then reconfigure the model deployment with resource limits.
  • D. Roll back the deployment to the previous model version.
  • E. Increase the overall resource allocation for the entire fleet.

Answer: C

Explanation:
Fleet Command's monitoring allows precise identification of resource bottlenecks. Resource limits prevent excessive consumption. Rolling back (A) is a reactive measure. Rebooting (B) is temporary. Increasing overall resources (D) is inefficient. Redeploying (E) is unlikely to solve the problem without investigation.


NEW QUESTION # 162
You are deploying a cloud VMI container and need to choose between different container runtimes (e.g., Docker, containerd, CRI-O).
Which factor is MOST crucial to consider when selecting a container runtime for a GPU-accelerated workload?

  • A. The runtime's performance overhead on CPU-bound tasks.
  • B. The runtime's compatibility with the NVIDIA Container Toolkit and its ability to expose GPUs to the container.
  • C. The ease of use and familiarity with the runtime.
  • D. The size of the container runtime image.
  • E. The runtime's security features and isolation capabilities.

Answer: B

Explanation:
For GPU-accelerated workloads, the critical factor is the container runtime's integration with the NVIDIA Container Toolkit and its ability to properly expose the GPUs to the container. Without this, the application will not be able to leverage the GPU.


NEW QUESTION # 163
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