NCP-AIN NVIDIA-Certified Professional AI Networking Questions and Answers

NVIDIA AIR (AI-Ready Infrastructure) is a cloud-based simulation platform designed to model and validate data center network deployments, including Spectrum-X Ethernet networks, using realistic topologies and configurations. When creating a custom topology in NVIDIA AIR, users can define network links between devices (e.g., spine and leaf switches) using a DOT file format, which is based on the Graphviz graph visualization software. The question asks for the correct syntax to define a link between port 1 on a spine switch (spine-01) and port 41 on a leaf switch (gpu-leaf-01) in a NVIDIA AIR simulation.

According to NVIDIA’s official NVIDIA AIR documentation, the DOT file format is used to specify network topologies, including nodes (devices) and links (connections between ports). The syntax for defining a link in a DOT file uses a double dash (--) to indicate a connection between two ports, with each port specified in the format " < node > ":" < port > ". For Spectrum-X networks, which typically use Cumulus Linux or SONiC on NVIDIA Spectrum switches, ports are commonly labeled as swpX (switch port X) rather than ethX (Ethernet interface), especially for switch-to-switch connections in a leaf-spine topology. The correct syntax for the link between port 1 on spine-01 and port 41 on gpu-leaf-01 is:

"spine-01":"swp01" -- "gpu-leaf-01":"swp41"

This syntax uses swp01 and swp41 to denote switch ports, consistent with Cumulus Linux conventions, and the double dash (--) to indicate the link, as required by the DOT file format.

Exact Extract from NVIDIA Documentation:

“You can create custom topologies in Air using a DOT file, which is the file type used with the open-source graph visualization software, Graphviz. DOT files define nodes, attributes, and connections for generating a topology for a network. The following is an example of a link definition in a DOT file:

"leaf01":"swp31" -- "spine01":"swp1"

This specifies a connection between port swp31 on leaf01 and port swp1 on spine01. Port names typically follow the switch port naming convention (e.g., swpX) for Cumulus Linux-based switches.”

— NVIDIA Air Custom Topology Guide

This extract confirms that option A is the correct answer, as it uses the proper DOT file syntax with swp01 and swp41 for port names and the double dash (--) for the link, aligning with NVIDIA AIR’s topology definition process for Spectrum-X simulations.

Analysis of Other Options:

B. "spine-01":"swp1" to "gpu-leaf-01":"swp41" : This option uses the correct port naming convention (swp1 and swp41) but incorrectly uses the word to as the connector instead of the double dash (--). The DOT file format requires -- to define links, making this syntax invalid for NVIDIA AIR.

C. "spine-01":"eth1" to "gpu-leaf-01":"eth41" : This option uses ethX port names, which are typically used for host interfaces (e.g., servers) rather than switch ports in Cumulus Linux or SONiC environments. Switch ports in Spectrum-X topologies are labeled swpX. Additionally, the use of to instead of -- is incorrect for DOT file syntax, making this option invalid.

D. "spine-01":"eth1" - "gpu-leaf-01":"eth41" : This option uses a single dash (-) instead of the required double dash (--) and incorrectly uses ethX port names instead of swpX. The ethX naming is not standard for switch ports in Spectrum-X, and the single dash is not valid DOT file syntax, making this option incorrect.

Why "spine-01":"swp01" -- "gpu-leaf-01":"swp41" is the Correct Answer:

Option A correctly adheres to the DOT file syntax used in NVIDIA AIR for defining network links:

Node and Port Naming : The nodes spine-01 and gpu-leaf-01 are specified with their respective ports swp01 and swp41, following the swpX convention for switch ports in Cumulus Linux-based Spectrum-X switches.

Link Syntax : The double dash (--) is the standard connector in DOT files to indicate a link between two ports, as required by Graphviz and NVIDIA AIR.

Spectrum-X Context : In a Spectrum-X leaf-spine topology, connections between spine and leaf switches (e.g., Spectrum-4 switches) use switch ports labeled swpX, making swp01 and swp41 appropriate for this simulation.

This syntax ensures that the NVIDIA AIR simulation accurately models the physical connection between spine-01 port 1 and gpu-leaf-01 port 41, enabling validation of the Spectrum-X network topology. The DOT file can be uploaded to NVIDIA AIR to generate the topology, as described in the documentation.

NetQ integrates multiple telemetry sources, including WJH , DOCA , and notably, Behavioral Telemetry .

From the NetQ Documentation – Behavioral Telemetry Section :

"Behavioral telemetry in NetQ correlates server and application behavior with network events, offering insights into root cause analysis by detecting anomalies in protocol, path, or performance behavior."

This helps identify patterns like:

Misbehaving applications causing retransmits.

Sudden changes in traffic flows.

Latency spikes correlated with app-level issues.

It complements device-level telemetry by introducing intent-based anomaly detection , crucial for proactive operations.

Incorrect Options:

Flow telemetry and packet capture offer raw data but not behavioral insights.

Application telemetry is too vague and is not the term NetQ uses for this feature.

Modern AI training (especially with LLMs) requires extremely high-speed, parallel access to large datasets. A dedicated storage fabric separates data I/O traffic from the training compute path and avoids contention.

From NVIDIA DGX Infrastructure Reference Architectures :

"Dedicated storage networks eliminate I/O bottlenecks by providing low-latency, high-bandwidth access to distributed storage for large-scale training jobs."

"Parallel access to datasets is key for performance, especially in multi-node, multi-GPU AI clusters."

Security (B) is important, but not the core reason for a storage fabric.

Cost (D) is typically increased , not reduced, with dedicated fabrics.

MLNX_OFED (Mellanox OpenFabrics Enterprise Distribution) is an NVIDIA-tested and packaged version of the OpenFabrics Enterprise Distribution (OFED) for Linux. It provides the necessary drivers and tools to support InfiniBand and Ethernet interconnects using the same RDMA (Remote Direct Memory Access) and kernel bypass APIs. MLNX_OFED enables high-performance networking capabilities essential for AI clusters, including support for up to 400Gb/s InfiniBand and RoCE (RDMA over Converged Ethernet).​

Reference Extracts from NVIDIA Documentation:

"MLNX_OFED is an NVIDIA tested and packaged version of OFED that supports two interconnect types using the same RDMA (remote DMA) and kernel bypass APIs called OFED verbs – InfiniBand and Ethernet."​

"Up to 400Gb/s InfiniBand and RoCE (based on the RDMA over Converged Ethernet standard) over 10/25/40/50/100/200/400GbE are supported."​

Virtual Routing and Forwarding (VRF) is the most effective method to achieve network segmentation and isolation in a multi-tenant environment.

From the NVIDIA Cumulus Linux Documentation – VRF Section :

"VRF allows multiple instances of routing tables to coexist within the same switch, effectively isolating traffic between tenants or departments."

Each department can:

Operate in its own VRF domain

Have independent routing tables

Maintain strict separation of Layer 3 paths

Incorrect Options:

A (Port Mirroring) – Used for traffic monitoring, not isolation.

C (ACLs) – Useful for fine-grained filtering, but not scalable tenant isolation.

D (LLDP) – Used for neighbor discovery, not security or isolation.

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 .

To upgrade the MLNX-OS on InfiniBand switches, the recommended procedure is as follows:​

Connect to the switch via SSH : Establish a secure shell connection to the switch using its management IP address.​

Fetch the MLNX-OS software image : Obtain the appropriate MLNX-OS software image from the official source or repository.​

Use the 'install' command to perform the upgrade : Execute the 'install' command on the switch to initiate the upgrade process with the fetched software image.​

This method ensures a smooth and efficient upgrade without the need for physical intervention or service disruption.​

Reference Extracts from NVIDIA Documentation:

"Click on Systems → MLNX-OS Upgrade. Select the desired upgrade method (e.g. 'Install from local file'). Select your image and click 'Install Image'."

An InfiniBand (IB) Router connects two or more InfiniBand subnets , making it possible for nodes in different subnets to communicate through route-managed communication .

From the official NVIDIA InfiniBand Routers Documentation :

"An InfiniBand router provides connectivity between two or more InfiniBand subnets, enabling communication between hosts that are not on the same subnet while preserving isolation and scalability."

Ensures fabric scalability by allowing subnet segmentation.

Uses LID routing across subnet managers (SMs).

Essential in large clusters with thousands of nodes.

Incorrect Options:

A and B are incorrect: InfiniBand does not connect directly to Ethernet or SANs without a gateway.

C is unrelated: NVLink is a GPU interconnect, not tied to InfiniBand routers.

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.

Exact Extract from NVIDIA Documentation:

“The ibnetdiscover tool is used to discover the InfiniBand fabric topology and generate a map of the network. It queries the subnet manager to retrieve information about all nodes, switches, and links in the fabric, providing a detailed view of the paths between nodes. This tool is critical for troubleshooting connectivity issues and ensuring proper routing in InfiniBand networks.”

— NVIDIA InfiniBand Networking Guide

This extract confirms that ibnetdiscover is the correct tool for discovering network paths in an InfiniBand east-west fabric. It provides a comprehensive view of the fabric’s topology, enabling administrators to trace paths between compute nodes and ensure seamless data flow.

To identify the active Subnet Manager (SM) node in an InfiniBand fabric, the correct command sequence is:

sminfo

Displays general information about the active SM in the fabric, including its LID .

smpquery ND < LID >

Resolves the Node Description (ND) at the given LID, revealing the exact hostname or label of the SM server.

From the InfiniBand Tools Guide:

"The sminfo utility provides the LID of the master SM. Use smpquery ND < LID > to resolve the node name hosting the SM."

This two-step approach is standard for locating and validating the SM identity in fabric diagnostics.

Incorrect Options:

B (Nl) is an invalid query type.

C and D do not identify SMs.

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.

Before upgrading the DOCA SDK on a BlueField DPU , it is mandatory to uninstall the existing OFED drivers to prevent compatibility conflicts.

From the NVIDIA DOCA Installation Guide :

"Before upgrading DOCA or BlueField-related software, you must remove existing OFED packages using: /usr/sbin/ofed_uninstall.sh -force."

This ensures:

Clean driver state

No residual kernel modules or userspace libraries

Proper registration of new DOCA/OFED versions

Incorrect Options:

A and C may not resolve conflicts.

D installs but doesn’t remove conflicting packages.

Role-Based Access Control (RBAC) is implemented within NVIDIA's Unified Fabric Manager (UFM) to manage user permissions effectively. RBAC allows administrators to assign roles to users, each with specific permissions, ensuring that only authorized individuals can make changes to the fabric configuration. This structured approach to access control enhances security by limiting the potential for unauthorized modifications and streamlines the management of user privileges across the network.​

For optimal performance in multi-node, multi-GPU AI workloads:

NVLink provides high-speed, low-latency communication between GPUs within the same node.

InfiniBand offers efficient, scalable communication between nodes in a data center. Combining these technologies ensures both intra-node and inter-node communication needs are effectively met.​

NVIDIA's What Just Happened (WJH) is a feature that provides real-time visibility into network problems by analyzing all packets passing through the switch and alerting on performance issues caused by packet drops, congestion, high latency, or misconfigurations.

WJH retains the last packets that were dropped from the switch with complete packet headers and the actual drop reason. This enhances the ability to debug network problems, identify affected flows, and decrease time-to-repair.

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.

Configuring NVUE to ignore certain underlying Linux files allows administrators to manage specific settings manually or through automation tools like Ansible without NVUE overwriting these configurations. This approach enables the persistent manipulation of settings using both NVUE and flat-file methods, providing flexibility in network management.

RoCE Adaptive Routing is a key feature of NVIDIA Spectrum-X that ensures high performance and resiliency in the network, even in the event of a link failure. This technology dynamically reroutes traffic to the least congested and operational paths, effectively mitigating the impact of link failures. By continuously evaluating the network's egress queue loads and receiving status notifications from neighboring switches, Spectrum-X can adaptively select optimal paths for data transmission. This ensures that the network maintains high throughput and low latency, crucial for AI workloads, even when certain links are down.​

Reference Extracts from NVIDIA Documentation:

"Spectrum-X employs global adaptive routing to quickly reroute traffic during link failures, minimizing disruptions and preserving optimal storage fabric utilization."​

"RoCE Adaptive Routing avoids congestion by dynamically routing large AI flows away from congestion points. This approach improves network resource utilization, leaf/spine efficiency, and performance."​