NCP-AIO Exam Dumps : NVIDIA AI Operations

Comprehensive and Detailed Explanation From Exact Extract:

When troubleshooting performance bottlenecks related to feeding data consistently to GPUs during deep learning workloads, the key storage metric to consider is sequential read speed. Deep learning training typically involves streaming large datasets sequentially from storage to GPUs. The sequential read speed measures how fast data can be read in a continuous stream, directly impacting the ability to keep GPUs fed without stalls.

Disk I/O operations per second (IOPS) measures random read/write operations and is less relevant for large sequential data streams in AI workloads.

Disk free space indicates available storage capacity but does not impact data feed rate.

Disk utilization in performance manager shows overall usage but does not specify the speed or consistency of data feed.

Therefore, focusing on sequential read speed (option C) is critical for ensuring consistent, high-throughput data feeding to GPUs, minimizing bottlenecks in deep learning runtime environments.

This is consistent with NVIDIA AI Operations best practices for system performance optimization and troubleshooting storage-related issues in AI infrastructure.

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Comprehensive and Detailed Explanation From Exact Extract:

Communication issues between UFM server and InfiniBand switches often result from misconfigured or missingsubnet manager configurationon the switches. The subnet manager controls fabric membership and routing, so verifying and correcting its setup is essential for proper UFM operation. Rebooting, adding GPUs, or disabling firewalls are less likely to resolve fabric-level communication problems.

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Comprehensive and Detailed Explanation From Exact Extract:

To restrict users’ access strictly to the GPUs allocated to their jobs, Slurm usescgroups (control groups)for resource isolation. Enabling device cgroup enforcement by settingConstrainDevices=yesincgroup.confenforces device access restrictions, ensuring jobs cannot access GPUs beyond those assigned.

Increasing memory allocation or setting job priorities does not restrict device access.

Modifying job scripts to request additional CPU cores does not limit GPU access.

Hence, enablingcgroup enforcement with ConstrainDevices=yesis the correct method to prevent users from accessing unallocated GPUs.