NCP-AIN
Free NVIDIA-Certified Professional: AI Networking Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$4006 exam domainsLevel Advanced2 timed practice tests in the course
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An AI factory has 8 GPUs per DGX node, each connected via NVLink internally and via a separate NIC to the network fabric for inter-node communication. Which topology design connects each GPU's NIC to a dedicated leaf switch such that GPU 0 across every node connects to leaf switch 0, GPU 1 to leaf switch 1, and so on?
Rail-optimized topology explicitly aligns each GPU's NIC to a dedicated rail (leaf) switch so that same-index GPUs across all nodes share a switch, minimizing hops for collective operations like NCCL all-reduce. This is the reference design used in NVIDIA DGX SuperPOD deployments.
A customer's InfiniBand fabric runs redundant Subnet Managers (SM) for high availability: SM-A on one management node and SM-B on another. During a planned maintenance window, the administrator manually fails mastership from SM-A to SM-B before powering down SM-A. After SM-A comes back online, it immediately reclaims master status and triggers a disruptive resweep of the fabric. Which configuration change would prevent SM-A from automatically reclaiming mastership when it rejoins?
OpenSM master election is based on sm_priority (and GUID as tiebreaker). Lowering SM-A's priority relative to SM-B ensures SM-B stays preferred master when both are active, preventing an unwanted takeover, while still keeping SM-A available as a standby for true redundancy.
An AI training cluster running RoCEv2 over a Spectrum-X Ethernet fabric is experiencing high tail latency at GPU-facing leaf switches, but PFC pause-frame counters remain low. The network team wants to reduce sender rates before congestion escalates to PFC pause storms and head-of-line blocking. Which approach BEST meets this requirement?
NVIDIA's programmable congestion control on Spectrum-X uses fine-grained telemetry (RTT and ECN signals) fed back to the NIC/DPU to proactively slow senders, addressing congestion before it escalates into PFC pauses and HOL blocking.
During a large AI training job, GPUDirect RDMA traffic experiences intermittent drops. The network operations team needs to identify the exact root cause (e.g., buffer overrun vs. CRC error vs. congestion) in real time without capturing full packet traces. Which tool should they use?
WJH is purpose-built for real-time, per-packet root-cause classification of drops (buffer overrun, CRC, ECN marking, tail drop, etc.) without requiring full packet capture, making it the fastest path to root cause.
A cloud provider runs a multi-tenant AI training cluster on a single InfiniBand fabric. Customer A's compute nodes must never be able to communicate with Customer B's compute nodes at the fabric level, even if both are on the same physical switches. Which mechanism should be configured to enforce this isolation?
P_Keys are the native InfiniBand mechanism for Layer 2 fabric isolation, restricting which HCAs can exchange traffic; UFM/OpenSM provisions and enforces partition membership across the subnet.
An AI data center architect needs to design an 800Gb/s non-blocking spine-leaf fabric for a 256-GPU cluster using Spectrum-X Ethernet, where each GPU server requires RDMA transport offload plus telemetry-based congestion signaling back to the fabric. Which hardware combination BEST meets these requirements?
Spectrum-4 switches provide the Spectrum-X telemetry and adaptive routing signaling plane, while BlueField-3 SuperNICs offload RoCEv2 transport, congestion response, and telemetry generation at the endpoint, together forming the full Spectrum-X Ethernet stack for AI clusters.
A company is deploying a 2048-GPU NVIDIA Quantum-2 InfiniBand cluster for large language model training and requires full bisection bandwidth to avoid job performance degradation caused by network oversubscription. Which topology design should the network architect implement?
A non-blocking (1:1) fat-tree guarantees equal bandwidth at every tier, delivering full bisection bandwidth so no path is oversubscribed — essential for large collective operations like allreduce during LLM training.
A network engineer notices intermittent throughput drops across a large-scale AI training cluster using NVIDIA Quantum InfiniBand. The engineer suspects congestion in a specific section of the fabric affecting multiple job placements. Which tool provides a real-time, cluster-wide view of port utilization and congestion hotspots to pinpoint the affected switches?
UFM Telemetry continuously aggregates port-level counters and congestion metrics across the entire fabric, making it the correct tool for real-time, cluster-wide hotspot identification.
A network engineer is deploying 200 new NVIDIA Spectrum-4 switches into a greenfield AI data center fabric. The requirement is to onboard each switch with zero manual console interaction — the switch should boot, pull its OS image and initial configuration automatically, and register with the fabric management tools. Which approach BEST meets this requirement?
Cumulus Linux-based Spectrum switches boot via ONIE and support Zero Touch Provisioning: DHCP option 239 (or a vendor-specific option) directs the switch to a URL for an ONIE installer image and/or a ZTP script that installs the OS and applies baseline configuration with no console access required — this is the standard NVIDIA-recommended method for at-scale onboarding.
A company is deploying an AI factory with 128 DGX H100 nodes (8 GPUs each, one ConnectX-7 NIC per GPU) for large-scale distributed LLM training. Which network design BEST minimizes GPU-to-GPU latency for collective operations (all-reduce, all-to-all) across nodes?
Rail-optimized topology ensures every GPU communicates with its counterpart GPU on other nodes through the same dedicated leaf switch (rail), minimizing hop count and contention for collective operations. This is the reference design pattern used in NVIDIA DGX SuperPOD/Spectrum-X deployments.
A team is deploying distributed GPU training pods across a Kubernetes cluster with NVIDIA ConnectX-7 NICs. Each pod requires a dedicated RoCEv2 RDMA interface for GPUDirect RDMA traffic in addition to the standard pod network, and the team wants automated lifecycle management of the MOFED drivers, device plugins, and CNI components across all nodes. Which NVIDIA solution BEST meets these requirements?
The NVIDIA Network Operator is purpose-built to automate deployment and lifecycle management of MOFED drivers, RDMA/SR-IOV device plugins, Multus CNI, and IPAM (Whereabouts) via the NicClusterPolicy CRD, enabling GPUDirect RDMA secondary interfaces on pods.
A cloud service provider must build a multi-tenant AI training-as-a-service platform that isolates tenant traffic, integrates with existing Ethernet-based operational tooling, and requires RoCEv2 for GPU-to-GPU communication. Which NVIDIA networking platform BEST meets these requirements?
Spectrum-X is purpose-built for Ethernet-based, multi-tenant AI clouds, combining adaptive routing and advanced congestion control (via Spectrum switches and BlueField DPUs) with RoCEv2 to deliver InfiniBand-like performance while keeping standard Ethernet operational tooling.
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How much does the NCP-AIN exam cost?
The exam fee is approximately $400 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 6 domains: NVIDIA Spectrum Networking (30%), NVIDIA InfiniBand Networking (30%), Troubleshooting Tools (20%), Automation and Configuration (10%), AI Data Center Design and Optimization (5%), Kubernetes Integration (5%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST34 at checkout for $34.99 (list undefined) through Oct 11 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.
AI Data Center Network Design and Optimization →InfiniBand Fabric Configuration and Management →Spectrum-X Ethernet and RoCE for AI →Network Automation, Monitoring, and Telemetry →Security, Troubleshooting, and Performance →NVIDIA Spectrum Networking →NVIDIA InfiniBand Networking →Troubleshooting Tools →