Know the AI stack
Tell AI, machine learning and deep learning apart, and explain the software layers NVIDIA provides.
Partners and press
Overview
Every model runs on someone’s hardware. This exam tests that you know what it takes.
NCA-AIIO is the Associate exam for people entering AI infrastructure and operations, with a basic grasp of data centers expected. It covers AI knowledge, then infrastructure at 40%: GPU scaling, power, cooling, networking, DPUs and cloud or on-premises. Operations make up the last 22%, and NVIDIA also recommends a technical degree and data center experience.
11Modules
50Questions
60Minutes
8Roles
What it tests
Learn all the way down.
Learn all the way down.
Perform when it matters.
Match hardware to work
Weigh what training and inference each need, and plan how GPU infrastructure scales.
Understand the facility
Recognize power, cooling, networking and DPU requirements, and the cloud versus on-premises trade-offs.
Follow the operations
Describe how clusters are orchestrated, jobs scheduled and GPUs monitored, and what virtualization adds.
Course outline
From neurons and tokens
From neurons and tokens
to racks, fabrics and schedulers.
We design this course ourselves. NVIDIA runs the exam and issues the credential, and LUNARTECH is independent of NVIDIA.
Start with the vocabulary. You separate AI, machine learning and deep learning, follow how a model is trained and then used, and see why these workloads put such particular demands on infrastructure.
- AI, ML and deep learning compared
- Neural networks in plain terms
- Training and what it needs
- Inference and what it needs
- Generative AI workloads
- Why AI workloads need accelerators
You build A one-page glossary mapping each AI term to the infrastructure it affects.
Hardware is only useful with software on top. You learn the layers NVIDIA provides, from drivers and CUDA to libraries, frameworks, containers and enterprise platforms, and what each layer is for.
- Drivers and the CUDA platform
- Libraries and frameworks above CUDA
- Containers and the NGC catalog
- Inference software and microservices
- NVIDIA AI Enterprise at a glance
- Where each layer can go wrong
You build A layered diagram of the stack with the role of each component.
A GPU is built for throughput, a CPU for flexibility. You compare their design, memory and cores, and learn why a modern AI server holds both and what each one does.
- Cores, threads and parallelism
- GPU memory and bandwidth
- CPU roles in a GPU server
- Tensor cores and precision
- Generations of GPUs and what changes
- Reading a GPU specification
You build A comparison table of CPU and GPU suitability for five workloads.
Training and inference stress a system differently. You weigh memory, compute and latency needs for each, and learn to match workloads to the right class of hardware.
- Training needs: memory, compute and communication
- Inference needs: latency and throughput
- Batch and real-time serving
- Model size against GPU memory
- Choosing a server class
- Cost against performance
You build A hardware recommendation for one training and one inference scenario.
One GPU rarely holds a real workload. You see how GPUs connect inside a server and across servers, how clusters scale out, and what changes at each step of growth.
- Multi-GPU servers and NVLink
- Scaling across nodes
- Why interconnect speed matters
- Cluster building blocks
- Single-node and cluster workloads
- Planning for growth
You build A scaling plan from one server to a small cluster, with the reasons for each step.
AI hardware draws power and gives off heat at a scale most rooms were not built for. You learn what these systems need, the limits of air cooling, and why liquid cooling is entering the data center.
- Power per server and per rack
- Heat and airflow basics
- Where air cooling runs out
- Liquid cooling at an overview level
- Efficiency and how it is measured
- Planning facility capacity
You build A power and cooling estimate for one AI rack, with the assumptions written out.
Networks decide how well GPUs work together. You learn why AI traffic needs fast, lossless links, what InfiniBand and Ethernet offer, and what a DPU takes off the host.
- East–west traffic in AI clusters
- InfiniBand and Ethernet at an overview level
- Storage networking for AI
- What a DPU does
- Security and isolation with DPUs
- Reading a network diagram
You build A network diagram for a small AI cluster, with each link’s purpose explained.
Where AI runs is a business choice with technical effects. You compare cloud, on-premises and hybrid, weigh cost, control and speed, and learn what each means for operations.
- Cloud GPU services and their limits
- On-premises AI infrastructure
- Hybrid and multi-cloud designs
- Cost models and utilization
- Data location and compliance
- Choosing a deployment model
You build A written comparison of cloud and on-premises for one organization’s AI plans.
A cluster is shared, so work needs organizing. You see how containers, Kubernetes and Slurm place work on GPUs, how queues and priorities work, and how teams share capacity.
- Containers for AI workloads
- Kubernetes at an overview level
- Slurm and job scheduling
- Queues, priorities and fair sharing
- GPU sharing and partitioning
- What an operator watches each day
You build A scheduling plan that shares one GPU cluster between two teams.
Operators need to see what the GPUs are doing. You learn the metrics that matter, the tools that report them, and how virtualization lets several users share hardware safely.
- GPU utilization, memory and temperature
- Monitoring tools and dashboards
- Health checks and alerts
- GPU virtualization overview
- Multi-Instance GPU and sharing
- Common warning signs
You build A monitoring checklist listing the metrics, tools and alert thresholds for an AI cluster.
Pull the topics into one plan. You take a stated AI workload and recommend the hardware, scale, facility, network and operations that would support it, and explain the trade-offs.
- Reading the workload
- Choosing hardware and scale
- Specifying power, cooling and network
- Planning operations
- Presenting trade-offs
You build An AI data center brief for one workload, covering hardware, facility, network and operations.
Issued by
Certifying body · Independent of LUNARTECH
NVIDIA
NVIDIA designs the GPUs and much of the software that AI runs on. It runs this certification program itself: the exams are online and proctored remotely, and passing earns a digital badge. LUNARTECH is independent of NVIDIA.
How you get there
Top-tier roles go to people who can do the work.
Top-tier roles go to people who can do the work.
Here’s how you get there.
Know where you start.
A basic understanding of data center infrastructure. NVIDIA also recommends a technical degree, enterprise data center experience and a grasp of AI and machine learning concepts.
Work through the course.
Eleven modules, designed by LUNARTECH, at your own pace.
Sit it with NVIDIA.
NVIDIA runs the exam and issues the credential, so you register and book directly with them.
Support
Support at every stage.
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All the way through
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After the last lesson
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Finish a LUNARTECH course and its certificate is issued in your name, under our signature. NVIDIA’s credential comes from NVIDIA, when you pass its exam.
Certificate of completion
Your Name
NCA-AIIO course
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Pricing
This course comes with the membership.
This course comes with the membership.
So does everything else.
One membership opens every course, certification, workshop and masterclass in the Academy. Every plan starts with a 3-day free trial. You register and book the exam directly with NVIDIA.
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Included in every plan
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In practice
Eight roles.
Eight roles.
One credential across them all.
No single job owns it. Here’s where each role leans hardest, and where its work ends up.
- 01AI knowledge
- 02Infrastructure
- 03Operations
-
Sizing a cluster starts with the workload. Training and inference want different hardware.
Infrastructure, Operations
AI data centers, Cloud providers
-
GPUs are scheduled and monitored like any asset. Know which metrics matter.
Operations, Infrastructure
Enterprise IT, AI data centers
-
Customers ask whether to build on-premises or rent. The trade-offs are part of this exam.
AI knowledge, Infrastructure
Cloud providers, Enterprise IT, Financial services
-
AI traffic behaves differently from ordinary enterprise traffic. DPUs and fabric choices are part of that picture.
Infrastructure
AI data centers, Telecom
-
Cloud or on-premises is a cost, control and speed decision. Infrastructure knowledge makes that call better.
Infrastructure, Operations
Cloud providers, Enterprise IT
-
Power and cooling limits shape every GPU deployment. Operations basics keep clusters running.
Infrastructure, Operations
AI data centers, Telecom
-
Budget holders need to know what GPUs actually require. The exam gives you the vocabulary for that conversation.
Infrastructure, Operations
Enterprise IT, Financial services
-
Customers ask what hardware their AI workload needs. Matching hardware to workloads is the core skill.
AI knowledge, Infrastructure
Cloud providers, Telecom
- AI data centers
- Cloud providers
- Enterprise IT
- Telecom
- Financial services
AI Infrastructure Engineer
Sizing a cluster starts with the workload. Training and inference want different hardware.
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Updated November 2026.
FAQ
Frequently asked questions.
Need help? These answers cover most of what people ask before they start.
No. NVIDIA issues it. LUNARTECH is independent of NVIDIA, and its names and marks belong to it. When you finish a course of ours, you earn a LUNARTECH certificate of completion for that course.
No. We design this course ourselves, around what the exam covers. NVIDIA has no part in it, and you still register and book the exam directly with NVIDIA.
50 questions, 60 minutes, online, proctored, in English.
A basic understanding of data center infrastructure. NVIDIA also recommends a technical degree, enterprise data center experience and a grasp of AI and machine learning concepts.
2 years. Retake the exam.
We last reviewed it on 5 November 2026, from NVIDIA’s own pages. Details change, so check the official page before you book.
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The course
Build the skills behind NCA-AIIO.
Prove you understand the infrastructure AI runs on. Start with a free trial, or enroll in a plan and work through the course at your own pace. NVIDIA runs the exam and issues the credential.
LUNARTECH is independent of the vendors and certifying bodies listed here. Their names, exam titles and marks belong to them, exams are run by them, and credentials are issued by them. Reviewed 5 November 2026. Catalogues change often, so confirm the details on the official page before you book.
