You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
NETAPPNetApp Certified AI Expert
The NetApp Certified AI Expert certification validates your ability to architect and manage AI infrastructures built on NetApp hybrid cloud solutions. Designed for technical professionals with six to twelve months of AI experience, it covers AI concepts, lifecycle, software and hardware architecture, and common challenges. Earning it demonstrates you can deliver reliable, high-performance AI workloads.
Exam formatScenario-based questions
Duration90 minutes
DeliveryPearson VUE
Free questions729
Content last reviewed 30 July 2026 · Up to date
The certification
What NetApp Certified AI Expert proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
5domains
31objectives
171concepts
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The NetApp Certified AI Expert certification validates the skills required to design, deploy, and manage AI infrastructures on NetApp hybrid cloud solutions. It is intended for technical professionals who build and operate AI workloads, covering NetApp AI solutions, AI concepts, the AI lifecycle, AI software and hardware architecture, and common challenges encountered in production environments.
Earning this credential demonstrates that you can architect and manage the data infrastructure underpinning AI initiatives, ensuring performance, scalability, and reliability. The exam is scenario-based, testing your ability to apply knowledge to real-world situations, and is a key step for professionals advancing their careers in AI infrastructure.
Who it’s for
This certification is for technical professionals who build and manage AI infrastructures, including solutions architects, data engineers, and infrastructure engineers. Candidates should have six to twelve months of hands-on experience with AI technologies and a solid understanding of NetApp hybrid cloud solutions. You are comfortable operating, provisioning, automating, configuring, and troubleshooting NetApp data solutions, and you understand the architectural requirements of AI workloads, from data ingestion to model training and inference.
Recommended experience
Six to twelve months of technical experience with AI, including building infrastructures for AI workloads, and familiarity with NetApp hybrid cloud solutions. Operating, provisioning, automating, configuring, and troubleshooting NetApp hybrid cloud data solutions; Understanding AI concepts, lifecycle, and common challenges; Knowledge of AI software and hardware architecture
The syllabus
What you’ll learn
Every domain and objective NetApp measures, with the weight they carry on the exam.
The official NetApp exam outline · checked 30 July 2026 · See the source
AI Overview
- Demonstrate the ability to train and inference
- Describe machine learning benefits
- Differentiate the use between different algorithm types (supervised, unsupervised, reinforcement)
- Describe how AI is used in varied industries
- Describe convergence of AI, high-performance computing, and analytics
- Determine the use of AI on-premises, in the cloud, and at the edge
AI Lifecycle
- Determine the differences between predictive AI and generative AI
- Describe the impact of predictive AI
- Describe the impact of generative text, images, videos, and decisions in Generative AI
- Determine how NetApp tools can enable data aggregating, cleansing, and modeling
- Determine the requirements needed for model generation
- Compare the differences between model building and fine-tuning models
- Determine the requirements needed for inferencing
AI Software Architectures
- Describe AI MLOps/LLMOps ecosystems and general use
- Determine the differences between Jupyter notebooks and pipelines
- Describe how the NetApp DataOps toolkit works
- Demonstrate the ability to execute AI workloads at scale with Kubernetes (Trident)
- Describe the uses of BlueXP software tools to build AI solutions
AI Hardware Architectures
- Describe data aggregation topologies (warehouses, data lakes, lakehouses)
- Describe compute architectures used with AI workloads (CPU, GPU, TPU, FPGA)
- Describe network architecture used with AI workloads (Ethernet vs Infiniband, RDMA, GPUDirect Storage)
- Identify storage architectures used with AI workloads (C-Series, A-Series, EF-Series, StorageGRID)
- Determine the use cases of different protocols
- Determine the benefits of SuperPOD architectures with NetApp
- Describe the use cases for BasePod and OVX architectures
AI Common Challenges
- Determine how to size storage and compute for training and inferencing workloads
- Describe the solutions for code, data, and model traceability
- Describe how to access and move data for AI workloads
- Describe solutions to optimize cost
- Describe solutions to secure storage for AI workloads
- Describe solutions to maximize performance in AI workloads
On the day
The exam itself
Everything NetApp publishes about sitting it, and nothing we inferred.
Prerequisites
Six to twelve months of technical experience with AI
CertificationNetApp Certified AI Expert
Exam formatScenario-based questions
Duration90 minutes
Questions60 questions
DeliveryPearson VUE
LanguagesEnglish, Japanese
Certification levelExpert
After you pass
Where this credential goes next
The path NetApp lays out, how the credential is kept, and where to book.
Step-by-step path to NetApp Certified AI Expert
PrerequisiteSix to twelve months of technical experience with AI
Credential earnedNetApp Certified AI Expert Expert level certification Lifecycle status
This certification is currently active and available. NetApp maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by NetApp
Exam registration
Register for the exam through Pearson VUE, NetApp’s authorized testing partner.
Schedule your examVisit the official NetApp certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How does the NetApp Certified AI Expert relate to the NetApp Certified AI Data Infrastructure Expert?
The NetApp Certified AI Data Infrastructure Expert is a higher-tier credential that requires passing the NetApp Certified AI Expert exam, the NetApp Certified Data Administrator exam, and one of two designated NVIDIA exams. Earning the AI Expert is a step toward the AI Data Infrastructure Expert.
Do I need to earn a lower-level NetApp certification before taking the NS0-901 exam?
No. NetApp does not require a lower-level certification as a prerequisite for the NS0-901 exam. However, candidates should have six to twelve months of AI experience and be able to operate NetApp hybrid cloud solutions.
Can I take the NS0-901 exam online?
Yes, the NS0-901 exam is available through Pearson VUE's OnVUE online proctoring service, as well as at test centers. Online testing requires meeting specific technology, environment, and identification requirements.
What are the online proctoring requirements for the NS0-901 exam?
You must run a system test, use a computer with Windows 10 or macOS 14 or higher, have a working webcam and microphone, use a single display, and ensure a quiet, distraction-free space. You must also present a valid government-issued ID and consent to monitoring.
What is the retake policy for the NS0-901 exam?
The official NetApp page does not specify a retake policy. For details, refer to the NetApp Certification FAQ or contact Pearson VUE customer service.
Are there any hands-on labs or performance-based tasks in the NS0-901 exam?
The exam consists of scenario-based questions. There is no mention of a separate hands-on lab component on the official exam page.
What job roles does the NetApp Certified AI Expert credential map to?
The credential is designed for technical professionals who build infrastructures for AI workloads, such as AI infrastructure engineers, solutions architects, and data engineers.
Can I recertify by passing a different NetApp exam?
NetApp certifications must be renewed every few years. Specific recertification options for the AI Expert are not published on the official exam page; check the NetApp Certification website for details.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 729 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept NetApp measures — each one written out.
5domains
31objectives
171concepts
160free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official NetApp exam outline · checked 30 July 2026 · See the source
1AI Overview148 questions
Demonstrate the ability to train and inference4 concepts · 6 pagesOpen →Describe machine learning benefits4 concepts · 6 pagesOpen →Differentiate the use between different algorithm types (supervised, unsupervised, reinforcement)7 concepts · 5 pagesOpen →Describe how AI is used in varied industries3 concepts · 5 pagesOpen →Describe convergence of AI, high-performance computing, and analytics7 concepts · 7 pagesOpen →Determine the use of AI on-premises, in the cloud, and at the edge5 concepts · 4 pagesOpen →
2AI Lifecycle172 questions
Determine the differences between predictive AI and generative AI11 concepts · 6 pagesOpen →Describe the impact of predictive AI6 concepts · 6 pagesOpen →Describe the impact of generative text, images, videos, and decisions in Generative AI5 concepts · 6 pagesOpen →Determine how NetApp tools can enable data aggregating, cleansing, and modeling4 concepts · 4 pagesOpen →Determine the requirements needed for model generation5 concepts · 5 pagesOpen →Compare the differences between model building and fine-tuning models5 concepts · 4 pagesOpen →Determine the requirements needed for inferencing7 concepts · 6 pagesOpen →
3AI Software Architectures144 questions
Describe AI MLOps/LLMOps ecosystems and general use3 concepts · 5 pagesOpen →Determine the differences between Jupyter notebooks and pipelines7 concepts · 6 pagesOpen →Describe how the NetApp DataOps toolkit works7 concepts · 6 pagesOpen →Demonstrate the ability to execute AI workloads at scale with Kubernetes (Trident)9 concepts · 8 pagesOpen →Describe the uses of BlueXP software tools to build AI solutions6 concepts · 6 pagesOpen →
4AI Hardware Architectures102 questions
Describe data aggregation topologies (warehouses, data lakes, lakehouses)5 concepts · 3 pagesOpen →Describe compute architectures used with AI workloads (CPU, GPU, TPU, FPGA)5 concepts · 5 pagesOpen →Describe network architecture used with AI workloads (Ethernet vs Infiniband, RDMA, GPUDirect Storage)3 concepts · 4 pagesOpen →Identify storage architectures used with AI workloads (C-Series, A-Series, EF-Series, StorageGRID)5 concepts · 2 pagesOpen →Determine the use cases of different protocols3 concepts · 3 pagesOpen →Determine the benefits of SuperPOD architectures with NetApp5 concepts · 4 pagesOpen →Describe the use cases for BasePod and OVX architectures3 concepts · 2 pagesOpen →
5AI Common Challenges163 questions
Determine how to size storage and compute for training and inferencing workloads5 concepts · 4 pagesOpen →Describe the solutions for code, data, and model traceability4 concepts · 6 pagesOpen →Describe how to access and move data for AI workloads7 concepts · 7 pagesOpen →Describe solutions to optimize cost5 concepts · 6 pagesOpen →Describe solutions to secure storage for AI workloads6 concepts · 5 pagesOpen →Describe solutions to maximize performance in AI workloads10 concepts · 8 pagesOpen →
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
Demonstrate the ability to train and inference6 pagesOpen →
Free in front of it
27 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
729 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
729questions
160pages
31objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1AI Overview148 questions
2AI Lifecycle172 questions
3AI Software Architectures144 questions
4AI Hardware Architectures102 questions
5AI Common Challenges163 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
171concepts
729free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from NetApp
Where the exam is defined, scheduled and scored.
3official links
171concepts mapped
The source
NetApp’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official NetApp outline already has questions here
We build from the official skills outline, not from a summary of it — 31 objectives, 171 concepts written under them, and free questions against every one. When NetApp changes the outline, this page changes with it.
31objectives on the outline→171concepts written→729free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.