NVIDIA-Certified Professional - AI Networking (NCP-AIN) - Fact Sheet¶

Quick Reference¶

Exam code: NCP-AIN
Duration: 2 hours
Questions: 70-75
Cost: $400 USD; NVIDIA says lab exercises are being added on November 1 (2026), when the price rises to $500
Level: Professional
Validity: 2 years from issuance
Prerequisites: 2-3 years of hands-on data center networking experience with NVIDIA hardware (recommended)
Passing score: Not published

📖 Official exam page

Exam Blueprint¶

Blueprint updated 2026-10-22 from NVIDIA's exam page (checked 2026-10-22). The notes below were written in May 2026 against an earlier domain list that NVIDIA no longer publishes, so they do not yet map one-to-one onto these topics; topics marked "not covered" need outside study.

Domain 1: InfiniBand Technology¶

InfiniBand Speed Generations¶

InfiniBand Protocol Layers¶

• Physical Layer - Signaling, encoding, link training
• Link Layer - Packet framing, flow control, error detection
• Network Layer - Routing, forwarding between subnets
• Transport Layer - End-to-end reliable delivery, QoS
• Upper Layer Protocols - Verbs API, MPI, storage protocols

Subnet Management¶

• Subnet Manager (SM) - Controls fabric configuration and routing
• Subnet Administrator (SA) - Provides fabric query interface
• OpenSM - open-source subnet manager
• UFM-based SM - NVIDIA's enterprise subnet manager
• Master/standby SM for high availability

Key Concepts¶

• LID (Local Identifier) - 16-bit address within a subnet
• GID (Global Identifier) - 128-bit globally unique address
• GUID - Hardware identifier for ports and nodes
• Partition Key (P_Key) - Network isolation mechanism
• Service Level (SL) - QoS classification
• Virtual Lane (VL) - Traffic separation within a link

📖 InfiniBand Documentation - NVIDIA InfiniBand guides

Domain 2: Spectrum Ethernet¶

NVIDIA Spectrum Switch Family¶

Cumulus Linux¶

• Linux-based network operating system for Spectrum switches
• Standard Linux networking tools (ip, bridge, iptables)
• NVUE - NVIDIA network configuration CLI
• Automation via Ansible, Puppet, Salt
• BGP, OSPF, EVPN-VXLAN support
• 📖 Cumulus Linux Docs - Switch OS documentation

NVIDIA DOCA¶

• Data-center infrastructure on a chip architecture
• SDK for DPU (BlueField) programming
• Network function acceleration
• Security and storage offload
• 📖 DOCA Documentation - DPU SDK

Ethernet for AI Features¶

• RoCE support for RDMA over Ethernet
• Priority Flow Control (PFC) for lossless Ethernet
• ECN (Explicit Congestion Notification)
• Large MTU support (jumbo frames)
• ECMP (Equal Cost Multi-Path) for load balancing

Domain 3: RDMA and RoCE¶

RDMA Fundamentals¶

Benefits: - Zero-copy data transfer (no CPU involvement) - Kernel bypass (user-space to NIC directly) - Low latency (microseconds) - High throughput (near wire speed) - Low CPU overhead

RDMA Operations: - Send/Receive - Two-sided operations - RDMA Write - One-sided remote memory write - RDMA Read - One-sided remote memory read - Atomic - Remote atomic operations (compare-and-swap)

RoCE Versions¶

RoCE v1: - RDMA over Ethernet Layer 2 - Uses Ethertype for RDMA packets - Limited to single broadcast domain (same VLAN) - No IP routing support

RoCE v2: - RDMA over UDP/IP - Routable across subnets - Standard choice for modern deployments - Requires lossless Ethernet (PFC or similar)

Lossless Ethernet Configuration¶

Priority Flow Control (PFC): - Per-priority pause mechanism - Prevents packet loss for RDMA traffic - Configured on specific traffic classes - Must be consistent across all switches in path - Risk of deadlock if misconfigured

Explicit Congestion Notification (ECN): - End-to-end congestion notification - Switches mark packets when queue exceeds threshold - Receivers echo congestion to senders - Senders reduce rate in response - Preferred over PFC for congestion management

DCQCN (Data Center QoS for Converged Networks): - Combines PFC and ECN - Rate-based congestion control for RoCE - Widely used in AI data centers - Implemented in ConnectX adapters

GPUDirect RDMA¶

• Direct data path between GPU memory and network adapter
• Bypasses CPU and system memory
• Requires GPU and NIC on same PCIe root complex (or NVSwitch)
• Essential for efficient multi-node GPU communication
• Used by NCCL for distributed training

📖 GPUDirect Documentation - GPU networking

Domain 4: Network Topologies for AI¶

Fat-Tree¶

• Standard leaf-spine architecture
• Full bisection bandwidth (non-blocking)
• Predictable latency
• Scales to hundreds of nodes
• Higher switch count at scale

Rail-Optimized¶

• Each GPU NIC connects to a dedicated rail switch
• Corresponding ranks across nodes share a rail
• Optimized for all-reduce communication pattern
• Fewer switches than full fat-tree
• Used in DGX SuperPOD reference architecture

Dragonfly¶

• Groups connected by global links
• Low diameter (few hops between any two nodes)
• Efficient for very large clusters
• More complex routing algorithms
• Lower cost at extreme scale

Adaptive Routing¶

• Dynamically select paths based on congestion
• Avoids hot spots in the fabric
• Supported by InfiniBand and Spectrum switches
• Improves effective bisection bandwidth
• Critical for large-scale AI workloads

Domain 5: UFM and Network Management¶

NVIDIA UFM (Unified Fabric Manager)¶

Core Features: - Fabric discovery and visualization - Subnet manager with HA support - Real-time monitoring and telemetry - Event management and alerting - Performance analysis and optimization - 📖 UFM Documentation

UFM Telemetry: - Per-port counters (data, errors) - Latency histogram collection - Congestion detection - Traffic pattern analysis - Historical trend reporting

UFM Health Monitoring: - Link error detection (symbol errors, CRC errors) - Cable health monitoring - Switch health status - Temperature and power monitoring - Automated fault isolation

Diagnostics Tools¶

• ibdiagnet - Comprehensive fabric diagnostics
• ibstat - Local HCA port status
• ibswitches - List switches in fabric
• perfquery - Performance counter query
• smpquery - Subnet management queries

Exam Tips¶

Key Concepts to Master¶

1. InfiniBand speed generations and bandwidth calculations
2. RoCE v1 vs v2 differences and configuration
3. PFC and ECN for lossless Ethernet
4. Fat-tree vs rail-optimized topologies
5. UFM capabilities and diagnostic tools
6. GPUDirect RDMA requirements and benefits