Guide
How to pass AI-901 AI Fundamentals
A three-to-four week route through AI workloads, machine learning, vision, language, generative AI, and exam-day tactics.
The exam
What is AI-901, and what happened to AI-900?
If you started studying for AI-900 and are seeing "AI-901" everywhere now, here's what changed.
- Microsoft retired AI-900 on 30 June 2026, and AI-901 is its replacement — the exam candidates book going forward, covering the same skills outline with a refreshed question pool.
- Already passed AI-900? That certification stands and you don't need to sit AI-901 too. Not taken either yet? AI-901 is the one to prepare for.
- AI-901 earns the Microsoft Certified: AI Fundamentals credential, pitched at technical and non-technical candidates alike — no data science background or programming required, just basic cloud and client-server familiarity.
- A sensible starting point for a project manager talking about an AI initiative or a developer about to specialise into AI-103, the AI App and Agent Developer Associate exam.
- Stays conceptual, not hands-on: recognise AI workload types, describe machine learning in plain language, know Microsoft's responsible-AI principles, and match a scenario to the right Azure AI service.
- Anchor your revision to the current outline rather than an old AI-900 course referencing retired service names.
Format & domains
Exam format and skills domains
AI-901 is entirely multiple choice and multi-select. There is no drag-and-drop ordering, no hotspot screenshots, and no multi-question case study to manage, which makes the format itself the easy part of this particular exam.
- QuestionsTypically 35–50; varies
- Duration45 minutes (65-minute seat)
- Pass score700 / 1000 (scaled)
- DeliveryOnline or a test centre
- ValidityDoesn't expire (Fundamentals)
- FeeVaries by country
The exam fee changes from country to country, so check Microsoft's certification page for the price where you live. The five skills domains and their weights, from the current outline, are below.
| Domain | Weight |
|---|---|
| Describe AI workloads and considerations | 15–20% |
| Describe fundamental principles of machine learning on Azure | 15–20% |
| Describe features of computer vision workloads on Azure | 15–20% |
| Describe features of NLP workloads on Azure | 15–20% |
| Describe features of generative AI workloads on Azure | 20–25% |
Generative AI is the single largest domain and the one Microsoft has expanded most in recent refreshes, so give it priority even though the other four are formally tied at 15–20% each. Computer vision and NLP reward the same underlying skill: matching a scenario to the exact Azure AI service that handles it.
Study plan
A realistic three-to-four week study plan
AI-901 needs considerably less time than an associate-level exam like AZ-104, since there is no hands-on configuration to practise — just concepts and vocabulary to pin down precisely. Four weeks at a gentle pace, or three if you already touch AI or ML tooling at work, is realistic for most candidates.
-
AI workloads and responsible AI
Questions often ask which single principle a situation violates, so learn the six by name.
- Identify the four workload types from a scenario: computer vision, NLP, document processing, generative AI.
- Fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability.
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Machine learning principles on Azure
Pin down the vocabulary before the services.
- Regression versus classification versus clustering; features versus labels; training versus validation data.
- A basic sense of the Transformer architecture.
- Azure Machine Learning: automated ML, data and compute services, model management and deployment.
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Computer vision and NLP workloads
Both domains reward the same skill: matching a scenario to the exact service.
- Image classification versus object detection versus OCR versus facial detection, mapped to Azure AI Vision or Azure AI Face.
- Key phrase extraction, entity recognition, sentiment analysis, speech recognition and synthesis, mapped to Azure AI Language and Azure AI Speech.
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Generative AI and exam sharpening
The largest domain, then the stretch where you prove it stuck.
- Generative AI scenarios (chat, code generation, summarisation), Microsoft Foundry, Azure OpenAI Service.
- The responsible-AI considerations specific to generative AI.
- Drill your weakest domain daily, then sit one or two full-length simulator runs before you book.
Where people lose marks
The hardest AI-901 topics
AI-901 uses closely related terms that are easy to confuse under time pressure. Give the areas below extra practice so you can tell them apart in a scenario.
The six responsible-AI principles
- The six sound similar in the abstract, and questions test whether you can name the one a scenario is really about.
- A model performing worse for one demographic group is a fairness problem, not a generic "ethics" issue — learn each with a concrete example.
Regression, classification, and clustering
- These three get confused constantly, particularly classification versus clustering.
- Classification predicts a known label from labelled data, clustering groups unlabelled data by similarity, regression predicts a continuous number.
Matching a computer-vision task to a service
- Four distinct tasks split across two services, and questions expect the specific task and service without hesitation.
- Identifying products in a shelf photo is object detection via Azure AI Vision; extracting a tax reference from a scan is OCR via the same service.
NLP scenario to the right Azure service
- The trap is a scenario mentioning both text and spoken input in one sentence.
- Key phrase extraction, entity recognition, sentiment analysis, and translation route through Azure AI Language; speech recognition and synthesis route through Azure AI Speech.
Generative AI's own responsible-AI considerations
- The largest domain is also the newest, and it repeats the responsible-AI theme with its own twists.
- Hallucination, content filtering, and grounding responses in your own data don't apply the same way to a classic classification model — don't assume week one's principles cover everything here.
How to practise
Practise the way the exam works
Watching videos leaves you able to recognise a term, not recall it, and AI-901 asks for precise vocabulary against the clock.
- Retrieval practice: answer from memory before the explanation loads — the effort of dragging the right term back is what fixes it.
- Spaced repetition: return to each domain just as it starts to fade; the responsible-AI principles you nailed in week one are exactly what you'll blur with generative AI's considerations by week four if you never revisit them.
- Full-length simulation: a timed, mixed-domain run at the exam's real 45-minute pace is the only way to find out whether you can sustain quick recall across 40 to 60 questions back to back.
- Run at least one before you book, and review every wrong answer rather than just noting the score.
Precision beats recognition
Azure Mastery writes a rationale for every option on every AI-901 question, so a wrong guess between classification and clustering, or vision and NLP, teaches you the distinction instead of just costing a mark. Exam IQ adds an on-device readiness score, and the adaptive plan circles back to your weak domain. Free to start, works offline.
Download Azure Mastery — freeExam day
Tactics for exam day
A few habits protect precise recall under the clock.
- Sitting online? Run the system check the day before and clear your desk — the proctor pans the camera around the room first.
- Arrive early either way; a rushed start eats into the concentration precise recall needs.
- Budget well under ninety seconds per question, and flag anything that makes you hesitate between two related terms with mark-for-review.
- Every question stands alone — no multi-question case studies here — so revisit flagged ones freely before you submit.
- Read each option carefully: wrong answers are usually real Azure AI services that are simply the wrong fit, so precision beats quick elimination.
- Remember the pass mark is 700 out of 1000, scaled. An unfamiliar scenario isn't a disaster: make your call and keep your pace.
Keep going
What to study after AI-901
AI-901 is a launchpad, not a destination — it gives you the vocabulary the next exams assume.
- Building generative AI applications and agents? AI-103 (AI App and Agent Developer Associate) is the natural next step: hands-on and SDK-aware, Python or .NET against Azure OpenAI and Azure AI Foundry, with a largest domain on Foundry agents, tool calling, and retrieval-augmented generation with Azure AI Search.
- There's no fork in the road any more: AI-103 replaced the retired AI-102 (AI Engineer Associate) on 30 June 2026, so it's the single Associate-level credential for AI developers.
- Budget two to three months after AI-901 — the jump from recognising terms to writing working code against the SDKs is a real one.
- Not sure AI is the right track, or wondering how it compares with Azure's infrastructure and security paths? Which Microsoft certification should you get first? walks through every tier and track.
Frequently asked
AI-901 FAQs
Is AI-901 the same exam as AI-900?
They cover the same skills outline, but Microsoft retired AI-900 on 30 June 2026 and AI-901 is its replacement — the exam you book going forward. If you already passed AI-900, that certification still stands and you do not need to sit AI-901 as well. If you have not yet taken either, AI-901 is the current exam.
How hard is the AI-901 exam?
Conceptual rather than hands-on — no code to write, no model to configure. The difficulty is precision: recognising the workload type, the responsible-AI principle at play, and the specific Azure AI service, especially in computer vision, where classification, detection, OCR, and facial detection must stay distinct. Some IT background: three to four weeks.
How long should I study for AI-901?
Three to four weeks for most candidates, compressing to two if you already work with ML or generative AI tools. Allow six to eight weeks if AI concepts are genuinely new, so the vocabulary — especially responsible-AI principles and ML terminology — has time to settle.
What is the passing score for AI-901?
700 out of 1000. The score is scaled rather than a raw percentage, so it doesn't map directly onto a fixed number of correct answers. Treat 700 as a comfortable target, not a line to scrape over.
Does the AI-901 certification expire?
No. AI-901 is a Fundamentals certification, and unlike Associate-level certifications such as AZ-104 or AI-103, Fundamentals credentials do not expire and carry no renewal requirement. Once you pass, the credential stands even as the underlying skills outline continues to update.
What should I study after AI-901?
The developer path now runs through a single exam. AI-103 (AI App and Agent Developer Associate) is the natural next step, expecting hands-on Python or .NET SDK work against Azure OpenAI and Azure AI Foundry. It replaced the retired AI-102 in June 2026, so this is where AI-901's vocabulary leads.
Ready to start on AI-901?
Free to start and it works offline: original AI-901 questions across all five domains, each with a rationale for every option and an on-device Exam IQ score with a confidence range.
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