AI Wisdom

NVIDIA professional level

NVIDIA Certified Professional: AI Operations

Code
NCP-AIO
Length
120 minutes
Price
$500
Valid for
2 years, renewed by retaking the exam
Study time, our estimate
90 to 140 hours

The only exam here where half your score comes from hands-on labs, which makes it hard to fake. It is for the people who keep GPU clusters running day to day. At $500 it is the most expensive exam on this list, matched by NCP-AIN once its labs are added, so try it after you have run a cluster.

Take it if

You operate GPU clusters and schedule workloads on them.

Skip it if

You have not worked on NVIDIA hardware. NVIDIA recommends 2 to 3 years of operations experience.

Before you start

Check yourself against this list first. If something is missing, close that gap before you pay for an exam.

  • Two to three years of operationsOn NVIDIA hardware, per NVIDIA.
  • Kubernetes and SlurmBoth appear in workload management.
  • Access to a labHalf the score is hands-on.

Helpful first: NCA-AIIO

All exam details
Format
30 multiple-choice questions plus 3 hands-on labs, in one session
Scoring
Pass or fail. Each section is 50% of the score.
Length
120 minutes
Price
$500
Valid for
2 years, renewed by retaking the exam
Delivery
Proctored, English
Experience
2 to 3 years operating NVIDIA hardware in a data center
If you fail
Wait 14 days

What the exam covers

  • Install and Deploy31%
  • Administration23%
  • Workload Management23%
  • Troubleshooting and Optimization23%

What to focus on

  • Installing and deploying the stack, the largest domain.
  • Administration of clusters, users and GPUs.
  • Workloads: Slurm, Kubernetes, the GPU Operator and MIG.
  • Troubleshooting from logs and GPU telemetry.
Everything on one mapEvery topic and key idea in this certification.
mindmap
  root((NCP-AIO))
    Install and deploy
      GPU Operator
      NGC
      Base Command Manager
    Administration
      MIG
      Users
      Health
    Workload management
      Device plugin
      Slurm containers
      Queues
    Troubleshoot
      Low utilization
      Throttling
      Practice
Why is this GPU job not running?A first-response path for pending and slow jobs.
flowchart TD
  A{"Job symptom"} -->|"Pod stays Pending"| B["Check device plugin and node GPU resources"]
  A -->|"Slow, GPUs idle"| C["Check the data loading path"]
  A -->|"Slow, GPUs hot"| D["Check throttling, power and cooling"]
  A -->|"Small jobs waste GPUs"| E["Configure MIG"]

Lesson 1 of 4, 31% of the exam

Install and deploy

The largest domain.

GPU Operator
Automates drivers and the device plugin on Kubernetes.
NGC
NVIDIA's catalog of GPU-optimized containers.
Base Command Manager
Provisions and manages clusters.
What the GPU Operator installsIt automates the software stack on each node.
flowchart TD
  A["GPU Operator"] --> B["NVIDIA driver"]
  A --> C["Kubernetes device plugin"]
  A --> D["Monitoring"]
  B --> E["Pods can request GPUs"]
  C --> E

Common trap

Hand-installing drivers on every node.

Try it yourself

Install the GPU Operator in a lab.

Official docs: NVIDIA GPU OperatorBase Command Manager

Lesson 2 of 4, 23% of the exam

Administration

Keeping GPUs shared fairly.

MIG
Partition GPUs for small jobs.
Users
Manage access and queues.
Health
Use DCGM for telemetry.
MIG partitionsOne GPU becomes several isolated instances.
flowchart LR
  A["One physical GPU"] --> B["Instance 1: job A"]
  A --> C["Instance 2: job B"]
  A --> D["Instance 3: job C"]

Common trap

Giving a small inference job a whole GPU.

Try it yourself

Create MIG partitions and schedule to them.

Official docs: MIG User Guide

Lesson 3 of 4, 23% of the exam

Workload management

Getting jobs to run.

Device plugin
Nodes advertise GPUs to the scheduler.
Slurm containers
Enroot runs unprivileged containers, and Pyxis integrates them.
Queues
Balance large and small jobs.
Containers on SlurmUnprivileged containers inside batch jobs.
sequenceDiagram
  participant U as User
  participant S as Slurm
  participant P as Pyxis
  participant E as Enroot
  U->>S: Submit a job with an image
  S->>P: Start the job step
  P->>E: Import and create the container
  E-->>U: Job runs on GPUs

Common trap

Debugging the app before checking GPU resources.

Try it yourself

Run a container through Slurm with Pyxis.

Official docs: Pyxis on GitHubEnroot on GitHub

Lesson 4 of 4, 23% of the exam

Troubleshoot

The labs will test this under time.

Low utilization
Usually GPUs waiting for data.
Throttling
Thermal or power limits.
Practice
Fix things against a clock.
Reading GPU symptomsUtilization tells you where to look.
flowchart TD
  A{"GPU utilization during a slow job"} -->|"Low"| B["Waiting for data"]
  A -->|"High, clocks down"| C["Thermal or power throttling"]
  A -->|"High, clocks normal"| D["Workload is simply heavy"]

Common trap

Spending all your time on the questions and rushing the labs.

Try it yourself

Break a node and fix it in 20 minutes.

Official docs: DCGM Diagnostics

Study plan

  1. Weeks 1 and 2: fundamentals

    Take AI Infrastructure and Operations Fundamentals.

  2. Weeks 3 to 5: labs

    Take the AI Operations Professional Workshop, or practice on real hardware. Set up the GPU Operator, MIG and a Slurm queue from scratch.

  3. Week 6: troubleshoot under time

    Break things on purpose and fix them against a clock. Half the exam is labs.

  4. Exam week

    Take the mock below for the multiple-choice half.

Exam day and after

  • NVIDIA certifications last two years, and you renew by retaking the exam.
  • Exams are proctored and in English. NVIDIA's FAQ covers both remote and in-person testing, and remote exams allow no breaks.
  • Check the exam page before you plan around a date. NVIDIA has been adding hands-on labs to professional exams.
  • Budget your time. The labs are worth half the score but sit in the same 120 minutes as the questions.
Test yourself8 practice questions, scored by exam topic.
Start the mock exam

Keep going

Exam facts checked on October 31, 2026 against NVIDIA certification page. Lessons are our own explanations, so use the official docs as the final word, and confirm price and dates before booking.