Aegis-GPU

NCP-AIN · Professional · 120 min · 70-75 questions · $400

AI Networking

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More study tools

  • Ask Aegis: the tutor button at the bottom right answers from the course content with citations.
  • Xid error reference: every in-use NVIDIA Xid code with NVIDIA's recommended actions.
  • HGX hardware guide: a beginner's tour of an 8-GPU server.

Exam facts

Level
Professional
Duration
120 minutes
Questions
70-75
Price
$400
Delivery
Online, remotely proctored
Language
English
Validity
Two years from issuance
Scoring
Pass/fail (no numeric score is reported)
Prerequisites
Two to three years of operational experience working in a data center with NVIDIA hardware solutions. The candidate should be able to deploy and manage NVIDIA AI networking infrastructure in support of AI workloads.

Exam facts verified against NVIDIA's NCP-AIN page on 2026-10-08. Always confirm on nvidia.com before booking.

Official blueprint

AI Data Center Design and Optimization — 5%
  1. 1.1 Describe an AI factory networking architecture and its components (e.g., GPUs, NVIDIA BlueField, scalable units, switches).
  2. 1.2 Describe rail-optimized topologies for high-performance AI workloads.
  3. 1.3 Describe GPU-to-GPU communications
NVIDIA Spectrum Networking — 30%
  1. 2.1 Configure NVIDIA Spectrum-X switches for RDMA over Converged Ethernet (RoCE) to enable high-speed, low-latency communication.
  2. 2.2 Enable and verify QoS, explicit congestion notification (ECN), and priority flow control (PFC), advanced features like adaptive routing, and telemetry
  3. 2.3 Configure multi-tenancy Border Gateway Protocol Ethernet VPN (BGP-EVPN) to isolate tenant workloads.
  4. 2.4 Use NVIDIA Air to simulate network environments and identify potential issues.
  5. 2.5 Diagnose congestion or packet loss using in-band telemetry and NVIDIA What Just Happened (WJH) services.
  6. 2.6 Use NetQ for real-time network monitoring, including congestion detection and latency measurements.
  7. 2.7 Install NVIDIA DOCA.
  8. 2.8 Configure NVIDIA SuperNIC functionality for advanced packet processing and congestion control.
NVIDIA InfiniBand Networking — 30%
  1. 3.1 Perform initial configuration and provisioning, including high availability (HA).
  2. 3.2 Configure partition keys (PKeys) to ensure secure multi-tenancy in InfiniBand networks.
  3. 3.3 Configure QoS and adaptive routing to dynamically adjust paths based on congestion.
  4. 3.4 Use UFM to monitor InfiniBand link status and bandwidth utilization.
Kubernetes Integration — 5%
  1. 4.1 Deploy the NVIDIA Network Operator to manage RDMA interfaces and InfiniBand networks within Kubernetes clusters.
  2. 4.2 Verify NVIDIA Network Operator functionality.
Troubleshooting Tools — 20%
  1. 5.1 Use tools like cl-resource-query to check resource allocation in Spectrum-X environments.
  2. 5.2 Use WJH services for real-time event analysis.
  3. 5.3 Verify low-latency interconnects between GPUs, CPUs, and storage systems.
  4. 5.4 Use UFM system health to diagnose InfiniBand issues
  5. 5.5 Use commands like ib\write\lat, ib\write\bw, ibping, ibstat, ibdiagnet, ibnodes, and iblinkinfo to diagnose connectivity issues.
Automation and Configuration — 10%
  1. 6.1 Manage Spectrum-X switch configurations through NVUE templates.
  2. 6.2 Write Ansible playbooks to automate network setup tasks like VLAN creation or RoCE configuration.

Study roadmap

  1. Baseline. Read the official exam page and study guide, then skim every domain below to find gaps.
  2. NVIDIA Spectrum Networking (30%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  3. NVIDIA InfiniBand Networking (30%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  4. Troubleshooting Tools (20%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  5. Automation and Configuration (10%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  6. AI Data Center Design and Optimization (5%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  7. Kubernetes Integration (5%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  8. Exam rehearsal. Re-read your weakest domain notes and the study guide.

Official resources

From NVIDIA