Pass the NVIDIA-Certified Professional NCP-AIO Questions and answers with ExamsMirror

Comprehensive and Detailed Explanation From Exact Extract:

Slow GPU-to-GPU communication in distributed systems often relates to theconfiguration of communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM. Ensuring these libraries are properly configured and optimized is critical for efficient GPU communication. Limiting GPUs or increasing RAM does not directly improve communication speed, and disabling InfiniBand would degrade performance.

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Comprehensive and Detailed Explanation From Exact Extract:

Before performing maintenance on a compute node in Slurm, the best practice is todrain the nodeto prevent new jobs from being scheduled while allowing current jobs to finish. This is done using thescontrol update NodeName= State=Draincommand or equivalent. Setting the node state to down immediately may disrupt running jobs, and disabling scheduling on all nodes is unnecessarily broad. Draining ensures a controlled transition for maintenance.

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Comprehensive and Detailed Explanation From Exact Extract:

The NVIDIA Virtual Machine Image (VMI) for machine learning provisioning automatically sets up anUbuntu Serverenvironment with essential components including Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver, andRapids—a suite of GPU-accelerated data science and analytics libraries. This comprehensive stack enables immediate development and deployment of ML workloads.

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Comprehensive and Detailed Explanation From Exact Extract:

Aheartbeat networkis a common technique used in HA clusters to continuously monitor the health and availability of cluster nodes. It allows nodes to detect failures and coordinate failover actions, thus preventing split-brain scenarios where multiple nodes believe they are active simultaneously, causing data corruption or conflicts. Manual failover, load balancers, or data replication alone do not prevent split-brain without this monitoring mechanism.

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Comprehensive and Detailed Explanation From Exact Extract:

TheBase View dashboardin NVIDIA Base Command Manager provides a centralized and real-time overview of GPU, CPU, and memory utilization across all nodes in the DGX SuperPOD cluster. This tool allows administrators to quickly identify bottlenecks and resource usage patterns efficiently, unlike manually checking logs or running commands node-by-node.

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Comprehensive and Detailed Explanation From Exact Extract:

The commandngc config setis used to configure the NGC CLI, including setting up API keys, access tokens, and other credentials necessary for full access to the Base Command Platform (BCP) CLI functionalities. This command enables users to authenticate and manage their access effectively.

Comprehensive and Detailed Explanation From Exact Extract:

The pre-configured GPU Operator in the NVIDIA Enterprise Catalog differs from the public NGC catalog GPU Operator primarily by its configuration to use aprebuilt vGPU driver imageand being configured to use theNVIDIA License System (NLS). These adaptations allow better support for enterprise environments where vGPU functionality and license management are critical.

Other options such as automatic installation of the Datacenter driver or additional installation of Network Operator are not specific differences highlighted between the two operators.

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Comprehensive and Detailed Explanation From Exact Extract:

NVIDIA Base Command Manager (BCM) provides aCluster Extension featurethat enables automatic provisioning and scaling of cloud resources (e.g., AWS) when on-premises capacity is fully utilized. This cloudbursting capability allows seamless extension of computational resources without manual intervention, improving flexibility and reducing downtime during peak demand. Options A, B, and C involve manual or incomplete automation approaches that do not leverage BCM’s integrated cluster extension functionality.

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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:

NVIDIA Fabric Manager is responsible for managing the fabric interconnect in HGX systems, including:

Configuring routing among NVSwitch ports (A)to optimize communication paths.

Coordinating with the NVSwitch driver to train NVSwitch-to-NVSwitch NVLink interconnects (C)for high-speed link setup.

Coordinating with the GPU driver to initialize and train NVSwitch-to-GPU NVLink interconnects (D)ensuring optimal connectivity between GPUs and switches.

Installing the GPU operator and vGPU driver is typically handled separately and not part of Fabric Manager’s core tasks.

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