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[General] Quiz High Hit-Rate NCP-AIO - Valid Real NVIDIA AI Operations Exam

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【General】 Quiz High Hit-Rate NCP-AIO - Valid Real NVIDIA AI Operations Exam

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NVIDIA AI Operations Sample Questions (Q47-Q52):NEW QUESTION # 47
You want to configure a Slurm cluster with heterogeneous nodes, some equipped with high-performance GPUs and others with only CPUs. Which Slurm configuration parameter allows you to define and utilize these different node types effectively?
  • A. NodeName=node[1-10] Gres=gpu:2 NodeName=node[11-20]
  • B. PartitionName=gpu PartitionName=cpu
  • C. QOS=gpu
  • D. Feature=gpu Feature=cpu
  • E. TRES= gpu:2
Answer: A
Explanation:
Using NodeName and Gres (Generic Resources), you can specify the resources available on each node. In this example, nodes 1- 10 are configured with 2 GPUs each, while nodes 11-20 are assumed to have only CPUs (no Gres specified).

NEW QUESTION # 48
You are the administrator of a Run.ai cluster with ACM enabled. You need to implement a chargeback mechanism to accurately track GPU usage and allocate costs to different research groups. What key pieces of information do you need to collect and what Run.ai and/or ACM features can help automate this process?
  • A. CPU utilization per job. This is the primary factor in determining costs.
  • B. GPU utilization per job, job duration, and associated research group. ACM and Run.ai provide APIs and dashboards for collecting this data, which can then be integrated with a billing system.
  • C. Average job completion time. Use this to distribute the cost equally.
  • D. Total number of jobs submitted by each group. Run.ai provides a summary of job submissions in the UI.
  • E. Network bandwidth used by each job. This is the best indicator of resource consumption.
Answer: B
Explanation:
For accurate chargeback, you need GPU utilization per job, job duration (to quantify resource usage over time), and the associated research group to whom the cost should be allocated. ACM and Run.ai provide APIs and dashboards for collecting this data, which can be integrated with a billing system for automated chargeback. While the total number of jobs submitted can be an indicator of activity, it doesn't reflect actual resource usage. CPU utilization and network bandwidth are less relevant than GPU utilization in a GPU-accelerated environment. Average job completion time is insufficient for equitable cost allocation.

NEW QUESTION # 49
A system administrator is experiencing issues with Docker containers failing to start due to volume mounting problems. They suspect the issue is related to incorrect file permissions on shared volumes between the host and containers.
How should the administrator troubleshoot this issue?
  • A. Reduce the size of the mounted volumes to avoid permission conflicts during container startup.
  • B. Reinstall Docker to reset all configurations and resolve potential volume mounting issues.
  • C. Use the docker logs command to review the logs for error messages related to volume mounting and permissions.
  • D. Disable all shared folders between the host and container to prevent volume mounting errors.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The first step to troubleshoot Docker container volume mounting issues is tocheck the container logsusingdocker logsfor detailed error messages, including those related to permissions. This provides direct insight into the cause of the failure. Reinstalling Docker or disabling shared folders are drastic steps and may not address the root cause. Volume size reduction is unrelated to permission conflicts.

NEW QUESTION # 50
You are tasked with monitoring the GPU utilization of a Run.ai cluster to identify potential bottlenecks and optimize resource allocation.
Which of the following metrics, available through the Run.ai UI or CLI, would be MOST useful for this purpose?
  • A. Disk I/O per container.
  • B. GPU memory utilization per job and per node.
  • C. Network bandwidth usage per pod.
  • D. Total number of jobs submitted.
  • E. CPU utilization per node.
Answer: B
Explanation:
GPU memory utilization per job and per node is the MOST useful metric for identifying GPU bottlenecks. It directly indicates how much of the available GPU memory is being used by each job and on each node, allowing you to identify overloaded nodes or jobs that are inefficiently using GPU resources. Other metrics are important for overall system monitoring, but GPU memory utilization is the key indicator for GPU-specific bottlenecks.

NEW QUESTION # 51
An organization only needs basic network monitoring and validation tools.
Which UFM platform should they use?
  • A. UFM Enterprise
  • B. UFM Pro
  • C. UFM Telemetry
  • D. UFM Cyber-AI
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
TheUFM Telemetryplatform provides basic network monitoring and validation capabilities, making it suitable for organizations that require foundational insight into their network status without advanced analytics or AI-driven cybersecurity features. Other platforms such as UFM Enterprise or UFM Pro offer broader or more advanced functionalities, while UFM Cyber-AI focuses on AI-driven cybersecurity.

NEW QUESTION # 52
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