How difficult is the NCP-AIO certification exam?

The exam is professional-level and requires 2-3 years of hands-on experience with NVIDIA data center hardware. It covers 4 domains: Installation & Deployment (31%), Administration (23%), Workload Management (23%), and Troubleshooting & Optimization (23%). Candidates must have practical experience with BCM, Slurm, Kubernetes, and GPU troubleshooting.

What is the passing score for this certification?

NVIDIA does not publicly disclose the exact passing score. The exam contains 30 multiple-choice questions plus 3 hands-on lab exercises within a 120-minute session. We recommend scoring 70%+ consistently on practice tests before scheduling.

How does NCP-AIO relate to NCA-AIIO and NCP-AII?

NCA-AIIO (Associate) covers foundational AI infrastructure knowledge. NCP-AII (Professional) validates infrastructure deployment skills. NCP-AIO (Professional) focuses specifically on day-to-day operations, monitoring, troubleshooting, and optimization. Together they form a complete AI infrastructure certification track.

What training is recommended?

NVIDIA recommends the 'AI Infrastructure & Operations Fundamentals' self-paced course and the 'AI Operations Professional Workshop' which covers DCGM, InfiniBand, BlueField DPUs, GPU virtualization, and cluster orchestration.

What is the exam fee?

The exam costs $500 USD. You'll need to create a Certiverse account to register.

Can I take the exam remotely?

Yes, the exam is delivered online and is remotely proctored via the Certiverse platform.

NVIDIA· Professional

NVIDIA-Certified Professional: AI Operations

NCP-AIO

The NCP-AIO exam tests whether you can install, administer, schedule and troubleshoot NVIDIA AI clusters with Base Command Manager, Slurm, Kubernetes and Run:ai. preporato.com covers each domain with timed practice exams, an explanation for every question and hands-on labs.

420+

問題数

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無期限

アップデート

含まれる内容

模擬試験

本番形式の模擬試験7セット

420問以上

Practice exam · NCP-AIOQ 1 / 65

Correct · explanation

実践

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# nca-aiio-resource-requests-limits · step 1

$ lab.check(1)

Step 1 Complete

NVIDIA-Certified Professional: AI Operations

含まれる内容

本番形式の模擬試験7セット

ラボ· 19

Kubernetes Resource Requests & Limits — Who Gets What, and Who Survives

Inside the NVIDIA GPU Operator — From Helm to Workload-Ready

GPU Container Lifecycle: Build, Test, Ship, Rollback

GPU Cost & Efficiency Audit

GPU Health Checks + Auto-Remediation

GPU Observability: From nvidia-smi to a Production Monitoring Stack

GPU Sharing: Streams, MPS, MIG, and the Real Cost of Contention

Inference Serving Patterns: Dynamic Batching, Throughput, and the Triton Mental Model

MLflow Experiment Tracking: From Single Run to Team Workflow

Nsight Systems Profiling: Finding the Bottleneck That Costs You 40% of Your GPU

Reproducible Training: The Flags, The Cost, The Artifacts

vLLM Production Serving: PagedAttention, Continuous Batching, Prefix Caching

NVIDIA GPU Operator on k3s: Single-Node Kubernetes for GPU Workloads

PriorityClass & Preemption — Who Survives the GPU Squeeze

Persistent Storage for AI Workloads — PVCs, StorageClass & the Checkpoint Pattern

Workload Controllers — Deployment, StatefulSet, DaemonSet for AI

Rolling Updates, Rollback & Blue-Green for AI Inference

Stuck-Pending Triage Day — Diagnose Any GPU Pod That Won't Run

Multi-GPU-Type Targeting — nodeSelector, nodeAffinity & Tolerations

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