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[InfiniBand Troubleshooting]
You are troubleshooting InfiniBand connectivity issues in a cluster managed by the NVIDIA Network Operator. You need to verify the status of the InfiniBand interfaces. Which command should you use to check the state and link layer of InfiniBand interfaces on a node?
To check the status and link layer of InfiniBand interfaces, the ibstat command is used. For example:
ibstat -d mlx5_0
This command provides detailed information about the InfiniBand device, including its state (e.g., Active), physical state (e.g., LinkUp), and link layer (e.g., InfiniBand).
[InfiniBand Troubleshooting]
A fabric administrator added new servers to a 40-port edge switch. The administrator now needs to gather and map the newly added ports' LIDs and LINK SPEED. Which of the following commands can be used for that purpose?
The correct utility is ibnetdiscover.
From the official NVIDIA InfiniBand Utilities Guide:
'ibnetdiscover scans the fabric and returns a topology of all switches and end nodes, including their GUIDs, LIDs, port numbers, and link speeds.'
It generates a fabric map with node-to-port relationships and shows:
GUIDs
LIDs (Local IDs)
Link speeds and widths
Switch-to-host connections
This is essential for network topology validation and mapping physical port additions.
Incorrect Options:
ib_check_routes -- for routing table diagnostics.
ibhosts -- shows host information but not switch-level port mapping.
ibswitches -- shows switch info, but lacks port-level LID/link speed mapping.
[AI Network Architecture]
Which of the following statements are true about AI workloads and adaptive routing?
Pick the 2 correct responses below.
AI workloads, particularly in large-scale training scenarios, are characterized by a small number of high-bandwidth, long-lived flows known as 'elephant flows.' These flows can dominate network traffic and are prone to causing congestion if not managed effectively.
Traditional flow-based load balancing mechanisms, such as Equal-Cost Multipath (ECMP), distribute traffic based on flow hashes. However, in AI workloads with low entropy (i.e., limited variability in flow characteristics), ECMP can lead to uneven traffic distribution and congestion on certain paths.
Adaptive routing techniques, which dynamically adjust paths based on real-time network conditions, are more effective in managing AI traffic patterns and mitigating congestion risks.
[Spectrum-X Configuration]
You are planning to deploy a large-scale Spectrum-X network for AI workloads. Before physical implementation, you want to validate the network design and configuration using a digital twin approach.
Which NVIDIA tool would be most appropriate for creating and simulating a digital twin of your Spectrum-X network?
NVIDIA Air is a cloud-based network simulation tool designed to create digital twins of data center infrastructure, including Spectrum-X networks. It allows users to model switches, SuperNICs, and storage components, enabling the simulation, validation, and automation of network configurations before physical deployment. This facilitates Day 0, 1, and 2 operations, ensuring that network designs are tested and optimized for AI workloads.
Reference Extracts from NVIDIA Documentation:
'NVIDIA Air enables cloud-scale efficiency by creating identical replicas of real-world data center infrastructure deployments.'
'NVIDIA Air allows users to model data center deployments with full software functionality, creating a digital twin. Transform and accelerate time to AI by simulating, validating, and automating changes and updates.'
'NVIDIA Air supports simulation of NVIDIA Spectrum Ethernet (Cumulus Linux and SONiC) switches and NVIDIA BlueField DPUs and SuperNICs as well as the NetQ network operations toolset.'
[Spectrum-X Configuration]
You are troubleshooting a Spectrum-X network and need to validate the fabric configuration. Which feature of Spectrum-X allows for automated fabric validation?
NVIDIA NetQ is a network operations tool that provides real-time visibility and automated validation of the network fabric. It helps in identifying misconfigurations, monitoring network health, and ensuring that the fabric meets the required specifications for AI workloads.
