PASS GUARANTEED QUIZ 2025 NVIDIA NCP-AIO: NVIDIA AI OPERATIONS–PROFESSIONAL LATEST EXAM MATERIALS

Pass Guaranteed Quiz 2025 NVIDIA NCP-AIO: NVIDIA AI Operations–Professional Latest Exam Materials

Pass Guaranteed Quiz 2025 NVIDIA NCP-AIO: NVIDIA AI Operations–Professional Latest Exam Materials

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NVIDIA AI Operations Sample Questions (Q42-Q47):

NEW QUESTION # 42
A team is running a large distributed training job across multiple nodes in your Run.ai cluster. They are experiencing significant performance degradation due to network latency between the nodes. What are the possible solutions you can implement with Run.ai and potentially ACM to mitigate this issue?

  • A. Implement data parallelism instead of model parallelism.
  • B. Enable RDMA (Remote Direct Memory Access) and ensure proper network configuration for low-latency communication.
  • C. Use Run.ai's built-in network acceleration features.
  • D. Increase the number of GPUs requested per node to reduce inter-node communication.
  • E. Configure node affinity rules to ensure that all nodes participating in the distributed training job are located within the same rack or network segment.

Answer: B,E

Explanation:
RDMA is a key technology for reducing network latency in distributed training. It allows direct memory access between GPUs on different nodes, bypassing the CPU and reducing overhead. Configuring node affinity to keep the nodes within the same rack or network segment minimizes physical distance and network hops, further reducing latency. Increasing GPUs per node can help but is not directly addressing the inter- node latency issue. Data vs. model parallelism is an application-level choice. Run.ai doesn't have built-in network acceleration as a specific feature, but it supports the underlying technologies like RDMA.


NEW QUESTION # 43
You are deploying a containerized AI application from NGC on a cluster with multiple GPU nodes. You want to ensure that the application is distributed across multiple GPUs and nodes for maximum performance. What strategies can you employ to achieve this?

  • A. Use a single container with multi-GPU support and configure it to utilize all available GPUs on the cluster.
  • B. Employ a message queue system like RabbitMQ or Kafka to distribute data to the containers running on different nodes.
  • C. Utilize a distributed training framework like Horovod or DeepSpeed to distribute the workload across multiple GPUs and nodes.
  • D. Configure Kubernetes resource quotas to limit the number of GPUs available to each container.
  • E. Deploy multiple replicas of the container, each configured to use a specific subset of GPUs on different nodes.

Answer: B,C,E

Explanation:
B, C, and E are correct. Deploying multiple container replicas allows for distribution across nodes. Distributed training frameworks manage workload distribution. A message queue facilitates data distribution to different nodes. A is incorrect as it relies on a single container handling all GPUs. D is used for resource management, not distribution.


NEW QUESTION # 44
Which statement BEST describes the role of NVIDIA's Cluster Manager (ACM) in a Run.ai environment?

  • A. ACM is used to manage storage and networking.
  • B. ACM provides advanced scheduling policies, fair-share algorithms, and resource management capabilities on top of Kubernetes, enhancing Run.ai's functionality.
  • C. ACM is a replacement for Kubernetes and manages the entire cluster infrastructure.
  • D. ACM is a tool for monitoring GPU utilization but does not directly impact scheduling or resource allocation.
  • E. ACM is only required for multi-cluster Run.ai deployments.

Answer: B

Explanation:
ACM (NVIDIA Cluster Manager) works in conjunction with Kubernetes and Run.ai. It provides advanced scheduling policies (like fair- share), enhanced resource management, and improved GPU utilization capabilities, supplementing Run.ai's core functionalities. ACM is not a replacement for Kubernetes. It enhances it. It does more than just monitoring. It's beneficial, not required, for multi-cluster setups. While ACM integrates with the underlying infrastructure, storage and networking management isn't its primary focus.


NEW QUESTION # 45
You are using 'nvsm' to manage your NVLink fabric. You want to verify the link speed and status between two specific GPUs. Which nvsm' command provides the MOST detailed information about individual NVLink connections?

  • A. 'nvsm show topology'
  • B. 'nvsm show configuration'
  • C. 'nvsm show health'
  • D. nvsm show links'
  • E. nvsm show devices'

Answer: D

Explanation:
'nvsm show links' provides detailed information about the individual NVLink connections, including their speed, status, and error counts. 'nvsm show topology' provides a high-level overview, while the other commands focus on different aspects of the system.


NEW QUESTION # 46
You have deployed the NVIDIA Device Plugin for Kubernetes on your BCM-managed cluster. After a kernel update on one of the worker nodes, the device plugin fails to discover the GPUs. The error messages indicate a mismatch between the driver version expected by the device plugin and the actual driver version installed on the node. What is the MOST reliable way to resolve this issue without disrupting other workloads?

  • A. Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.
  • B. Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.
  • C. Remove the NVIDIA Device Plugin and replace it with the 'nvidia-driver-installer' helm chart
  • D. Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.
  • E. Use a DaemonSet to manage the NVIDIA driver installation on all worker nodes, ensuring a consistent driver version across the cluster and compatibility with the device plugin.

Answer: E

Explanation:
Using a DaemonSet to manage the NVIDIA driver installation is the MOST reliable and scalable solution. It ensures that all worker nodes have the correct driver version and simplifies driver updates. Manually downgrading or updating individual nodes (A, B) is not sustainable. Reinstalling the toolkit (D) might not update the driver. Simply removing and replacing the plugin (E) doesn't address driver mismatch and would likely use a similar deployment method that would lead to the same error.


NEW QUESTION # 47
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