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

The ib_write_bw tool is part of the Perftest package and is specifically designed to measure the bandwidth of RDMA write operations between two InfiniBand endpoints. It provides accurate assessments of RDMA throughput, which is crucial for verifying the performance of InfiniBand networks in high-performance computing and AI training environments.​

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.

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).​

In the Spectrum-X architecture,BlueField-3 SuperNICsare responsible for executing the congestion control algorithm. They handle millions of congestion control events per second with microsecond reaction latency, applying fine-grained rate decisions to manage data flow effectively. This ensures optimal network performance by preventing congestion and packet loss.​

The ibdiagnet utility is a fundamental tool for InfiniBand fabric discovery, error detection, and diagnostics. It provides comprehensive reports on the fabric's health, including error reporting, switch and Host Channel Adapter (HCA) configuration dumps, various counters reported by the switches and HCAs, and parameters of devices such as switch fans, power supply units, cables, and PCI lanes. Additionally, ibdiagnet performs validation for Unicast Routing, Adaptive Routing, and Multicast Routing to ensure correctness and a credit-loop-free routing environment.​

Reference Extracts from NVIDIA Documentation:

"The ibdiagnet utility is one of the basic tools for InfiniBand fabric discovery, error detection and diagnostic. The output files of the ibdiagnet include error reporting, switch and HCA configuration dumps, various counters reported by the switches and the HCAs."​

"ibdiagnet also performs Unicast Routing, Adaptive Routing and Multicast Routing validation for correctness and credit-loop free routing."​

In Cumulus Linux, microbursts are short-lived, high-volume traffic bursts that often go undetected by coarse-grained monitoring like SNMP.

The two tools specifically used for this purpose are:

What Just Happened (WJH)

"WJH provides real-time packet drop visibility and classifies drops by reason (e.g., congestion, ACLs, etc.), enabling microburst detection."

ASIC monitoring at millisecond granularity

"Deep telemetry is enabled via the switch ASIC, which provides sub-second counters that capture microburst patterns otherwise missed by SNMP."

Incorrect Options:

AandCprovide low-frequency sampling, insufficient for microbursts which last milliseconds.

The SN5600 smart-leaf/spine/super-spine switch offers 64 ports of 800GbE in a dense 2U form factor. The SN5600 offers diverse connectivity in combinations of 1 to 800GbE and boasts an industry-leading total throughput of 51.2Tb/s.​

A rail-optimized topology is designed to enhance GPU-to-GPU communication by connecting each GPU's Network Interface Card (NIC) to a dedicated rail switch. This configuration ensures predictable traffic patterns and minimizes network interference between data flows, which is crucial for the performance of large-scale AI workloads, such as training large language models. By reducing contention and latency, this topology supports efficient and scalable AI training environments.​

Reference Extracts from NVIDIA Documentation:

"Rail-optimized network topology helps maximize all-reduce performance while minimizing network interference between flows."​

"A Rail Optimized Stripe Architecture provides efficient data transfer between GPUs, especially during computationally intensive tasks such as AI Large Language Models (LLM) training workloads, where seamless data transfer is necessary to complete the tasks within a reasonable timeframe."​

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 lowentropy (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.​

TheNVIDIA UFM Cyber-AI Platformis specifically designed to enhance security and operational efficiency in InfiniBand data centers. It leverages AI-powered analytics to detect security threats, operational anomalies, and predict potential network failures. By analyzing real-time telemetry data, it identifies abnormal behaviors and performance degradation, enabling proactive maintenance and threat mitigation.​

This platform integrates with existing UFM Enterprise and Telemetry services to provide a comprehensive view of the network's health and security posture. It utilizes machine learning algorithms to establish baselines for normal operations and detect deviations that may indicate security breaches or hardware issues.​

The NVIDIA NVUE Collection for Ansible includes pre-built roles designed to streamline automation tasks across various switch models and configurations. These roles encapsulate common network configurations, allowing for efficient and consistent deployment.​

By utilizing these roles, network administrators can:​

Apply standardized configurations across different devices.​

Reduce the complexity of playbooks by reusing modular components.​

Ensure consistency and compliance with organizational policies.​

This approach aligns with Ansible best practices, promoting maintainability and scalability in network automation.​

The correct utility isibnetdiscover.

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.

NVIDIA NetQ is a highly scalable network operations toolset that provides visibility, troubleshooting, and validation of networks in real-time. It delivers actionable insights and operational intelligence about the health of data center networks—from the container or host all the way to the switch and port—enabling a NetDevOps approach.

NetQ can be used as the functional test platform for the network CI/CD in conjunction with NVIDIA Air. Customers benefit from testing the new configuration with NetQ in the NVIDIA Air environment (“digital twin”) and fix errors before deploying to their production. ​

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

sminfo

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

smpquery ND <LID>

Resolves theNode 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.

CandDdo not identify SMs.

NVIDIA BlueField Data Processing Units (DPUs)are integral to securing AI infrastructures, especially in environments requiring stringent data privacy and security measures. BlueField DPUs offload and accelerate critical infrastructure tasks such as encryption, firewall enforcement, and intrusion detection, thereby isolating sensitive data paths from potential threats.​

In the context of AI workloads, BlueField DPUs enable secure and efficient data movement between GPUs and storage systems, ensuring that sensitive information, like financial data, is protected during both training and inference processes. Their integration into NVIDIA's reference architectures provides a hardware root of trust, essential for maintaining data integrity and compliance with security standards.​

The mlxlink tool is used to check and debug link status and issues related to them. The tool can be used on different links and cables (passive, active, transceiver, and backplane). It is intended for advanced users with appropriate technical background.​

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.​

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."​