NCP-AIO NEW TEST CAMP | NCP-AIO NEW DUMPS QUESTIONS

NCP-AIO New Test Camp | NCP-AIO New Dumps Questions

NCP-AIO New Test Camp | NCP-AIO New Dumps Questions

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NVIDIA AI Operations Sample Questions (Q41-Q46):

NEW QUESTION # 41
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. The NVIDIA driver version used on the host node is automatically inherited by the container; no specific configuration is needed.
  • B. Bake the required NVIDIA driver version directly into the container image during the Docker build process.
  • C. Specify the desired driver version in the Kubernetes Deployment manifest using the 'nvidia.com/gpu.driver.version' resource limit.
  • D. Configure the Kubernetes node's operating system to always use the desired NVIDIA driver version globally.
  • E. Utilize the NVIDIA Device Plugin for Kubernetes and configure it to inject the correct driver libraries into the container during runtime.

Answer: B,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 # 42
You're using BCM to manage a cluster and need to upgrade the Kubernetes version. What considerations are critical to ensure a smooth upgrade process?

  • A. Test the upgrade in a staging environment before applying it to the production cluster.
  • B. Update the NVIDIA drivers and container runtime on all nodes after the Kubernetes upgrade.
  • C. Drain nodes before upgrading them to minimize application downtime.
  • D. Back up the etcd database before starting the upgrade.
  • E. Ensure all worker nodes have sufficient resources (CPU, memory) for the new Kubernetes version.

Answer: A,C,D,E

Explanation:
Backing up etcd is crucial for rollback. Resource sufficiency prevents upgrade failures. Testing in staging identifies potential issues. Draining minimizes downtime. NVIDIA drivers should ideally be checked for compatibility and potentially updated before the Kubernetes upgrade.


NEW QUESTION # 43
What is the primary benefit of using NVIDIA MIG in a multi-tenant environment?

  • A. Guaranteed isolation and resource allocation for each tenant.
  • B. Improved CPU performance.
  • C. Simplified container deployment.
  • D. Increased network bandwidth.
  • E. Decreased memory usage.

Answer: A

Explanation:
MIG's primary benefit is to provide guaranteed isolation and resource allocation for each tenant in a multi-tenant environment. This ensures that each tenant has dedicated GPU resources and that their workloads do not interfere with each other.


NEW QUESTION # 44
You observe that some of your AI training pods are being preempted by higher-priority pods, leading to wasted GPU resources and prolonged training times. How can you mitigate this issue while still ensuring that high-priority jobs can run?

  • A. Disable preemption entirely on the Kubernetes cluster.
  • B. Lower the priority of the higher-priority pods.
  • C. Configure PodDisruptionBudgets (PDBs) for the AI training pods to minimize disruptions.
  • D. Use taints and tolerations to dedicate specific nodes to AI training pods and prevent preemption.
  • E. Increase the resource requests for the AI training pods to prevent preemption.

Answer: C

Explanation:
The correct answer is B. PodDisruptionBudgets (PDBs) allow you to define a minimum number of replicas that must be available at all times, preventing voluntary disruptions (including preemption) from affecting the training jobs too severely. Option A might delay preemption but won't prevent it if higher-priority pods still need resources. Option C could disrupt other important workloads. Option D isolates AI training, potentially underutilizing resources. Option E is generally not recommended as it can lead to scheduling issues for critical workloads.


NEW QUESTION # 45
A system administrator needs to collect the information below:
* GPU behavior monitoring
* GPU configuration management
* GPU policy oversight
* GPU health and diagnostics
* GPU accounting and process statistics
* NVSwitch configuration and monitoring
What single tool should be used?

  • A. Nsight Systems
  • B. CUDA Toolkit
  • C. nvidia-smi
  • D. DCGM

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
TheNVIDIA Data Center GPU Manager (DCGM)is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.
* nvidia-smiprovides GPU monitoring but lacks full policy and NVSwitch management.
* CUDA Toolkit is for GPU programming and development.
* Nsight Systems is focused on performance profiling and debugging.
Therefore, DCGM is the single tool that meets all the listed requirements.


NEW QUESTION # 46
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