Free Practice Questions for NVIDIA NCP-AIN Exam

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.​

TheCloudAI Benchmarkis designed to evaluate and establish performance baselines in AI-optimized networking environments like NVIDIA Spectrum-X. It assesses various performance metrics, including throughput and latency, ensuring that the network meets the demands of AI workloads. This benchmarking is essential for validating the benefits of Spectrum-X and for ongoing performance monitoring.​

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.

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.​

TheLocal Route Header (LRH)in InfiniBand is termed "local" because it is used exclusively for routing packets within a single subnet. The LRH contains the destination and source Local Identifiers (LIDs), which are unique within a subnet, facilitating efficient routing without the need for global addressing. This design optimizes performance and simplifies routing within localized network segments.​

InfiniBand is a high-performance, low-latency interconnect technology widely used in AI and HPC data centers, supported by NVIDIA’s Quantum InfiniBand switches and adapters. The Local Routing Header (LRH) is a critical component of the InfiniBand packet structure, used to facilitate routing within an InfiniBand fabric. The question asks why the LRH is called a “local header,” which relates to its role in the InfiniBand network architecture.

According to NVIDIA’s official InfiniBand documentation, the LRH is termed “‘local’ because it contains the addressing information necessary for routing packets between nodes within the same InfiniBand subnet.” The LRH includes fields such as the Source Local Identifier (SLID) and Destination Local Identifier (DLID), which are assigned by the subnet manager to identify the source and destination endpoints within the local subnet. These identifiers enable switches to forward packets efficiently within the subnet without requiring global routing information, distinguishing the LRH from the Global Routing Header (GRH), which is used for inter-subnet routing.

Exact Extract from NVIDIA Documentation:

“The Local Routing Header (LRH) is used for routing InfiniBand packets within a single subnet. It contains the Source LID (SLID) and Destination LID (DLID), which are assigned by the subnet manager to identify the source and destination nodes in the local subnet. The LRH is called a ‘local header’ because it facilitates intra-subnet routing, enabling switches to forward packets based on LID-based forwarding tables.”

—NVIDIA InfiniBand Architecture Guide

This extract confirms that option A is the correct answer, as the LRH’s primary function is to route traffic between nodes within the local subnet, leveraging LID-based addressing. The term “local” reflects its scope, which is limited to a single InfiniBand subnet managed by a subnet manager.

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.

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.​

Role-Based Access Control (RBAC)is implemented within NVIDIA's Unified Fabric Manager (UFM) to manage user permissions effectively. RBAC allows administrators toassign 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.​

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.​

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.