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NVIDIA NCP-AIN Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA-Certified Professional AI Networking (NCP-AIN) Exam
Exam Number:NCP-AIN
Certificate Validity Period:2 years
Exam Price:$400 USD
Exam Duration:120 minutes
Related Certifications:NVIDIA-Certified Professional AI Operations (NCP-AIO)
NVIDIA-Certified Professional AI Infrastructure (NCP-AII)
Real Exam Qty:70-75
Passing Score:Pass/Fail only, no exact score published
Available Languages:Chinese, English
Exam Format:Multiple choice questions, Single/multiple select items
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:2–3 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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NVIDIA NCP-AIN Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Network Architecture: This section of the exam measures the skills of AI Infrastructure Architects and covers the ability to distinguish between AI factory and AI data center architectures. It includes understanding how Ethernet and InfiniBand differ in performance and application, and identifying the right storage options based on speed, scalability, and cost to fit AI networking needs.
Topic 2
  • InfiniBand Configuration, Optimization, Security, and Troubleshooting: This section of the exam measures the skills of Data Center Network Administrators and covers the configuration and operational maintenance of NVIDIA InfiniBand switches. It includes setting up InfiniBand fabrics for multi-tenant environments, managing subnet configurations, testing connectivity, and using UFM to troubleshoot and analyze issues. It also focuses on validating rail-optimized topologies for optimal network performance.
Topic 3
  • Spectrum-X Configuration, Optimization, Security, and Troubleshooting: This section of the exam measures the skills of Network Performance Engineers and covers configuring, managing, and securing NVIDIA Spectrum-X switches. It includes setting performance baselines, resolving performance issues, and using diagnostic tools such as CloudAI benchmark, NCCL, and NetQ. It also emphasizes leveraging DPUs for network acceleration and using monitoring tools like Grafana and SNMP for telemetry analysis.

NVIDIA-Certified Professional AI Networking Sample Questions (Q22-Q27):

NEW QUESTION # 22
You are setting up PKey memberships for different tenants in an InfiniBand network. You want to ensure that some tenants have limited communication capabilities. Which PKey membership type allows members to communicate with full members but not with other members of the same type?

Answer: D

Explanation:
In InfiniBand networks, P_Keys (Partition Keys) control communication boundaries. Each port can belong to one or more partitions with either full or limited membership.


NEW QUESTION # 23
Which command should be used to configure RoCE on a Cumulus switch?

Answer: A

Explanation:
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 .


NEW QUESTION # 24
You're designing a multi-GPU system for AI training using NVIDIA GPUs with NVLink connections.
You need to maximize inter-GPU communication bandwidth. Which feature included in NCCL allows for improved communication between GPUs and NICs?

Answer: A

Explanation:
The correct answer isPXN (Peer eXchange Network).
From theNVIDIA NCCL Documentation:
"PXN enables communication between GPUs connected via NVLink and NICs by treating the GPUs as a distributed switch. This architecture improves bandwidth utilization by enabling any GPU to communicate with the NIC via the shortest path available, even if it's not directly connected to the NIC." This enhances GPU-to-NIC and NIC-to-GPU transfers, leveraging the NVLink topology. It significantly boosts performance in multi-GPU setups where not every GPU is directly connected to the NIC.
Other options:
* Adaptive Routingis a fabric-level feature for dynamic path rerouting.
* Graph Search Optimizationis used internally for topology modeling in NCCL.
* SHARP v2is a switch-based collective acceleration method, unrelated to PXN.
Reference: NVIDIA NCCL User Guide - PXN Feature Section


NEW QUESTION # 25
You have recently implemented NVIDIA Spectrum-X in your data center to optimize AI workloads. You need to verify the performance improvements and create a baseline for future comparisons.
Which tool would be most appropriate for creating performance baseline results in this Spectrum-X environment?

Answer: B

Explanation:
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.
Reference:NVIDIA Spectrum-X Validated Solution Stack


NEW QUESTION # 26
What is the benefit of using NVIDIA's GPU-accelerated networking in AI inference?

Answer: C

Explanation:
GPU-accelerated networking reduces latency and increases throughput, making it ideal for AI inference tasks that require real-time, low-latency data processing.


NEW QUESTION # 27
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