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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional AI Networking (NCP-AIN) Exam |
| Exam Number: | NCP-AIN |
| Exam Price: | $400 USD |
| Exam Duration: | 120 minutes |
| Passing Score: | Pass/Fail only, no exact score published |
| Exam Format: | Multiple choice questions, Single/multiple select items |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Infrastructure (NCP-AII) |
| Available Languages: | Chinese, English |
| Real Exam Qty: | 70-75 |
| Recommended Training: | NVIDIA InfiniBand Essentials NVIDIA Spectrum Networking Training |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AIN Sample Questions |
| Exam Way: | Online remote proctored exam |
| Pre Condition: | 2โ3 years of data center operation experience with NVIDIA hardware; ability to deploy and manage AI networking infrastructure supporting AI workloads |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-networking-professional/ |
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NEW QUESTION # 84
As the network administrator for a large-scale AI research cluster, you are responsible for ensuring seamless data flow across an InfiniBand east-west fabric that interconnects hundreds of compute nodes. Which tool would you use to trace and discover the network paths between nodes on this InfiniBand east-west fabric?
Answer: B
Explanation:
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.
NEW QUESTION # 85
You are using NVIDIA Air to simulate a Spectrum-X network for AI workloads. You want to ensure that your network configurations are optimal before deployment. Which NVIDIA tool can be integrated with Air to validate network configurations in the digital twin environment?
Answer: C
Explanation:
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.
NEW QUESTION # 86
Which tool would you use to gather telemetry data in a SpectrumX network?
Answer: C
Explanation:
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.
NEW QUESTION # 87
Which of the following NCCL environment variables enable SHARP aggregation with NCCL when using the NCCL-SHARP plugin?
Pick the 2 correct responses below
Answer: A,C
Explanation:
To enable SHARP (Scalable Hierarchical Aggregation and Reduction Protocol) aggregation using theNCCL- SHARP plugin, the following two environment variables are required:
* NCCL_COLLNET_ENABLE=1
Enables NCCL's support for CollNet (Collective Network) operations, including SHARP.
* NCCL_SHARP_AUTOINIT=1
Automatically initializes the SHARP plugin when available, activating SHARP-based collectives.
From theNVIDIA NCCL User Guide - SHARP Plugin Section:
"NCCL_COLLNET_ENABLE must be set to enable collective network acceleration features."
"NCCL_SHARP_AUTOINIT enables automatic SHARP plugin integration at NCCL runtime." Incorrect Options:
* B. NCCL_ALGO=CollNet- This variable controls the algorithm used for collectives but does not enable SHARP.
* C. NCCLSPECTRUM_ENABLE- This is not a documented NCCL variable.
Reference: NCCL SHARP Plugin Guide & NCCL User Guide - Environment Variables Section
NEW QUESTION # 88
Which component of the Spectrum-X platform is responsible for reordering out-of-order packets?
Answer: A
Explanation:
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.
NEW QUESTION # 89
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P.S. Free 2026 NVIDIA NCP-AIN dumps are available on Google Drive shared by Exam4Free: https://drive.google.com/open?id=11qx0g72zHvggXX2KlrE1dtokATGS0X8T