The NVIDIA Spectrum-X networking platform is an Ethernet-based solution optimized for AI workloads, combining Spectrum-4 switches, BlueField-3 SuperNICs, and advanced software to deliver high performance and low latency. Gathering telemetry data is critical for optimizing Spectrum-X networks, as it provides visibility into network performance, congestion, and potential issues. The question asks for the tool used to collect telemetry data in a Spectrum-X network.
According to NVIDIA's official documentation, NVIDIA NetQ is the primary tool for gathering telemetry data in Ethernet-based networks, including those running on Spectrum-X platforms with Cumulus Linux or SONiC. NetQ is a network operations toolset that provides real-time monitoring, telemetry collection, and analytics for network health, enabling administrators to optimize performance, troubleshoot issues, and validate configurations. It collects detailed telemetry data such as link status, packet drops, latency, and congestion metrics, which are essential for Spectrum-X optimization.
Exact Extract from NVIDIA Documentation:
"NVIDIA NetQ is a highly scalable network operations tool that provides telemetry-based monitoring and analytics for Ethernet networks, including NVIDIA Spectrum-X platforms. NetQ collects real-time telemetry data from switches and hosts, offering insights into network performance, congestion, and connectivity. It supports Cumulus Linux and SONiC environments, making it ideal for optimizing Spectrum-X networks by providing visibility into key metrics like latency, throughput, and packet loss."
-NVIDIA NetQ User Guide
This extract confirms that option C, NetQ, is the correct tool for gathering telemetry data in a Spectrum-X network. NetQ's integration with Spectrum-X switches and its ability to collect and analyze telemetry data make it the go-to solution for network optimization tasks.

Comprehensive and Detailed 150 to 250 words of Explanation From NVIDIA AI Networking Topics:
The correct answer is B. nv set qos roce enable on . In NVIDIA Cumulus Linux, RoCE configuration is managed under the QoS hierarchy in NVUE because RoCE depends on lossless Ethernet behavior, priority flow control, congestion management, and related quality-of-service mechanisms. NVIDIA's official Cumulus Linux documentation shows that the RoCE feature is configured through nv set qos roce , and in newer command references this appears explicitly as nv set qos roce enable on|off . This confirms both the command structure and the ordering of the keywords: nv set # qos # roce # enable on .
The other options are incorrect because they do not follow valid NVUE syntax. NVIDIA documents NVUE commands in a strict object-based order, where the top-level feature group comes immediately after nv set .
Since RoCE is implemented as part of the QoS framework, qos must appear before roce . Commands such as nv set roce qos enable on , nv qos roce enable on , and nv roce qos enable on do not match the official NVIDIA command model. Therefore, for enabling RoCE on a Cumulus switch in an NVIDIA AI networking environment, the verified command is nv set qos roce enable on .

The ibnetdiscover utility is used to perform InfiniBand subnet discovery and outputs a human- readable topology file. GUIDs, node types, and port numbers are displayed, as well as port LIDs and node descriptions. All nodes and links are displayed, providing a full topology. This utility can also be used to list the current connected nodes. The output is printed to the standard output unless a topology file is specified.
InfiniBand is a high-performance, low-latency interconnect technology used in AI and HPC data centers, particularly for east-west traffic between compute nodes in large-scale fabrics. Ensuring seamless data flow requires tools to troubleshoot and monitor the network, including the ability to trace and discover network paths between nodes. The question asks for the specific tool used to trace and discover paths in an InfiniBand fabric, which is a key task in InfiniBand troubleshooting.
According to NVIDIA's official InfiniBand documentation, the ibnetdiscover tool is designed to discover and map the topology of an InfiniBand fabric, including the paths between nodes. It scans the fabric, queries the subnet manager, and generates a topology map that details the connections between switches, Host Channel Adapters (HCAs), and other devices. This tool is essential for verifying connectivity, identifying routing paths, and troubleshooting issues like misconfigured routes or link failures in large-scale InfiniBand fabrics.

Comprehensive and Detailed 150 to 250 words of Explanation From NVIDIA AI Networking Topics:
The correct answer is A. Cyber-AI platform . NVIDIA's official UFM Cyber-AI documentation states that the UFM Cyber-AI platform determines a data center's unique "vital signs" and uses them to identify performance degradation, component failures, and abnormal usage patterns . This directly matches the requirement for proactive detection of issues in a large InfiniBand fabric. NVIDIA also documents that Cyber- AI provides Link Anomaly detection and Link Failure Prediction , where machine learning models analyze monitoring information and predict future link failures 1 to 24 hours in advance . In addition, NVIDIA's Cyber-AI materials describe integration with AI cybersecurity capabilities to help detect and mitigate security threats , which makes Cyber-AI the only option in this list that combines predictive analytics with security- oriented intelligence. By contrast, UFM Telemetry focuses on collecting and presenting real-time network data such as bandwidth, congestion, latency, and errors, while UFM Enterprise is the broader management platform and Host Agent is not the AI analytics engine for predictive failure and threat detection. Therefore, for AI-driven analytics, predictive maintenance, anomaly detection, and security-focused analysis in InfiniBand environments, Cyber-AI platform is the verified NVIDIA answer.

In InfiniBand networks, Partitioning using Partition Keys (PKeys)is the standard method for implementing multi-tenancy and traffic isolation. PKeys allow administrators to define logical partitions within the fabric, ensuring that traffic is confined to designated groups of nodes. This mechanism is essential for creating secure and isolated environments in multi-tenant architectures. The Unified Fabric Manager (UFM) leverages PKeys to manage these partitions effectively, enabling administrators to assign and control access rights across different tenants.
This approach ensures that each tenant's traffic remains isolated, maintaining both security and performance integrity within the shared fabric.