NVIDIA NCP-AIN NVIDIA-Certified Professional AI Networking Exam Practice Test

The NVIDIA Spectrum-X Reference Architecture (RA) 1.0.1 is designed for Ethernet AI cloud deployments and includes the SN5600 Spectrum-4 switches and BlueField-3 SuperNICs. This architecture supports adaptive routing and DOCA programmable congestion control (PCC) for lossless RoCE traffic, optimizing performance for AI workloads.

The SN5600 switch offers 64 ports of 800GbE in a dense 2U form factor, providing high throughput and low latency essential for AI applications.

Within the Spectrum-X platform, the NVIDIA BlueField-3 SuperNIC is responsible for reordering out-of-order packets. When RoCE adaptive routing is employed, packets may arrive at their destination out of order due to dynamic path selection. The BlueField-3 SuperNIC handles this by reassembling the packets in the correct order at the transport layer, ensuring that the application receives data seamlessly.​

Reference Extracts from NVIDIA Documentation:

"As different packets of the same flow travel through different paths of the network, they may arrive out of order to their destination. At the RoCE transport layer, the BlueField-3 DPU takes care of the out-of-order packets and forwards the data to the application in order."​

"The BlueField-3 SuperNIC offers adaptive routing, out-of-order packet handling and optimized congestion control."​

The NVIDIA Spectrum-X networking platform is an Ethernet-based solution optimized for AI workloads, combining Spectrum-4 switches, BlueField-3 SuperNICs, and software like DOCA and NetQ to deliver high performance, low latency, and efficient data transfer. A key feature of Spectrum-X is its adaptive routing, which dynamically selects the least-congested paths for packet transmission to maximize bandwidth and minimizelatency. However, this per-packet load balancing can result in packets arriving out of order at the destination, necessitating a mechanism to reorder them for seamless application performance. The question asks which Spectrum-X component is responsible for reordering these out-of-order packets.

According to NVIDIA’s official documentation, theBlueField-3 SuperNICis the component responsible for reordering out-of-order packets in the Spectrum-X platform. The SuperNIC, a network accelerator designed for hyperscale AI workloads, handles packet reordering at the RDMA over Converged Ethernet (RoCE) transport layer. It uses its processing capabilities to transparently reorder packets and place them in the correct sequence in the host memory, ensuring that adaptive routing’s out-of-order delivery is invisible to the application. This is critical for maintaining predictable performance in AI workloads, particularly for GPU-to-GPU communication in Spectrum-X networks.

Exact Extract from NVIDIA Documentation:

“The Spectrum-4 switches are responsible for selecting the least-congested port for data transmission on a per-packet basis. As different packets of the same flow travel through different paths of the network, they may arrive out of order to their destination. The BlueField-3 SuperNIC transforms any out-of-order data at the RoCE transport layer, transparently delivering in-order data to the application.”

—NVIDIA Technical Blog: Turbocharging Generative AI Workloads with NVIDIA Spectrum-X Networking Platform

This extract confirms that option A, the SuperNIC (specifically the BlueField-3 SuperNIC), is the correct answer. The SuperNIC’s role in reordering packets ensures that the adaptive routing implemented by Spectrum-4 switches does not compromise application performance, maintaining high effective bandwidth and low tail latency for AI workloads.

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.

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'."

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."​

Configuring NVUE to ignore certain underlying Linux files allows administrators to manage specific settings manually or through automation tools like Ansible withoutNVUE overwriting these configurations. This approach enables the persistent manipulation of settings using both NVUE and flat-file methods, providing flexibility in network management.

NetQintegrates multiple telemetry sources, includingWJH,DOCA, and notably,Behavioral Telemetry.

From theNetQ 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 introducingintent-based anomaly detection, crucial for proactive operations.

Incorrect Options:

Flow telemetryandpacket captureoffer raw data but not behavioral insights.

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

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.​

TheNVIDIA DGX SuperPODis a turnkey AI supercomputing infrastructure designed for large-scale deep learning and high-performance computing workloads. It integrates multiple DGX systems with high-speed networking and storage solutions, providing a scalable and efficient platform for AI research institutions. The architecture supports rapid deployment and is optimized for training complex models, making it the ideal choice for environments demanding top-tier AI performance.​

Accessing theBlueField DPU Operating System (OS)is possible throughrshim, either over PCIe or USB, and viaSSH through the OOB interfacewhen in DPU mode.

From theNVIDIA BlueField Software Documentation:

"You can access the BlueField OS through the rshim interface. The rshim module enables host-to-DPU communication either via PCIe (default) or USB."

B. rshim over PCIe: Default when BlueField is installed in a host.

D. rshim over USB: Useful for provisioning or systems without PCIe drivers.

Incorrect Options:

A (NIC mode): BlueField acts as a transparent NIC; OS access is not available to the host.

C (Redfish): Redfish is for out-of-band management, not direct OS-level access.

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.​

The "What Just Happened" (WJH) service on Cumulus switches provides real-time visibility into network problems by monitoring various layers and components, including layer 1, layer 2, layer 3, tunnel, buffer, and Access Control List (ACL) related issues. WJH streams detailed and contextual telemetry data, enabling administrators to diagnose and troubleshoot network problems effectively.​

Reference Extracts from NVIDIA Documentation:

"WJH can monitor layer 1, layer 2, layer 3, tunnel, buffer and ACL related issues."​

"The WJH service enables you to diagnose network problems by looking at dropped packets."​

Direct Memory Access (DMA) in InfiniBand networks allows data to be transferred directly between the memory of two devices without involving the CPU. This capability significantly reduces CPU overhead, lowers latency, and increases throughput, making it ideal for AI workloads that demand efficient data transfers.

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

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

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

EVPN Multi-homingenablesactive-active redundancy without inter-switch linksby usingoverlay routingover VXLAN and distributed control plane using BGP EVPN.

From the officialNVIDIA Cumulus Linux EVPN Multihoming Documentation:

"EVPN multihoming allows multiple Top-of-Rack (ToR) switches to connect to the same server while maintaining full layer-2 redundancy without the need for inter-switch links or traditional MLAG configuration."

Key benefits:

Simplified topology (no ISL/peer-link needed)

BGP-based control plane

Fast convergence

Active-active links per host NIC

Incorrect Options:

MLAGrequires ISL between switches and peer-link configuration.

VSS(Virtual Switching System) is a Cisco term, not supported in NVIDIA networking.

InDPU Mode, theARM cores on BlueFieldown theNIC data path, while still allowing thehost system to access the DPU OS (via OOB or virtio).

From NVIDIA BlueField Documentation:

"In DPU Mode, the data path is offloaded to the BlueField Arm cores, enabling advanced security and networking functions, while still allowing host access to the BlueField OS."

This is different from:

NIC Mode: Data path controlled by host, ARM cores inactive.

Separated Host Mode: Complete isolation; host cannot access DPU OS.

Restricted Mode: Limited host access to DPU OS, but without full offload capabilities.

Before upgrading Cumulus Linux, it's essential to back up configuration files to a different server. The /etc directory is the primary location for all configuration data in Cumulus Linux. Specifically, the following files and directories should be backed up:​

/etc/frr/ - Routing application (responsible for BGP and OSPF)

/etc/hostname - Configuration file for the hostname of the switch

/etc/network/ - Network configuration files, most notably /etc/network/interfaces and /etc/network/interfaces.d/

/etc/cumulus/acl - Access control list configurations​

Cumulus Linux is a network operating system used on NVIDIA Spectrum switches, including those in the Spectrum-X platform, to provide a Linux-based environment forEthernet networking in AI and HPC data centers. When upgrading Cumulus Linux to a new version, it’s critical to migrate specific configuration files to preserve network settings and ensure continuity. The question asks for the two configuration file locations that should be migrated from the old installation during an upgrade.

According to NVIDIA’s official Cumulus Linux documentation, the key directories containing configuration files that should be migrated during an upgrade are /etc/cumulus/acl (for access control list configurations) and /etc/network (for network interface configurations). These directories store critical network settings that define the switch’s behavior, such as ACL rules and interface settings, which must be preserved to maintain network functionality after the upgrade.

Exact Extract from NVIDIA Documentation:

“When upgrading Cumulus Linux, you must back up and migrate specific configuration files to ensure continuity of network settings. The following directories should be included in the backup:

/etc/cumulus/acl: Contains access control list (ACL) configuration files that define packet filtering and security policies.

/etc/network: Contains network interface configuration files, such as interfaces and ifupdown2 settings, which define the network interfaces and their properties.Back up these directories before upgrading and restore them after the new version is installed to maintain consistent network behavior.”—NVIDIA Cumulus Linux Upgrade Guide

This extract confirms that options A and B are the correct answers, as /etc/cumulus/acl and /etc/network contain essential configuration files that must be migrated during a Cumulus Linux upgrade. These files ensure that ACL policies and network interface settings are preserved, which are critical for Spectrum-X configurations in AI networking environments.

NVIDIA NetQ is a comprehensive network operations tool designed to provide real-time visibility into the health and performance of NVIDIA networking environments, including Spectrum-X. It offers detailed telemetry and analytics, allowing administrators to monitor adaptive routing behaviors, detect congestion, and analyze traffic patterns. By leveraging NetQ, you can ensure that adaptive routing is functioning as intended and that the network is optimized for AI workloads.​

Reference Extracts from NVIDIA Documentation:

"The NVIDIA NetQ network validation and ASIC monitoring tool set provide visibility into the network health and behavior. The NetQ flow telemetry analysis shows the paths that data flows take as they traverse the network, providing network latency and performance insights."​

"By leveraging telemetry from Spectrum Ethernet switches and BlueField-3 SuperNICs, NVIDIA NetQ can detect network issues proactively and troubleshoot network issues faster for optimal use of network capacity."​

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.

Adaptive Routingin InfiniBand networks dynamically selects the optimal path for data packets based on current network conditions, such as congestion levels and link utilization. This approach ensures that traffic is evenly distributed across the network, preventing bottlenecks and maximizing overall throughput.​

By continuously monitoring the network and adjusting routes in real-time, Adaptive Routing enhances performance and reliability, making it the preferred choice for high-performance computing environments where consistent low latency and high bandwidth are critical.​

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.​