The current Microsoft AI-901 study guide measures two broad abilities: identifying AI concepts and capabilities (40–45%) and implementing introductory solutions with Microsoft Foundry (55–60%). That is a different learning path from the retired AI-900 exam, which emphasized the recognition of older Azure AI service categories. A useful study routine should test both halves: classify an unfamiliar customer request as text analysis, speech, vision, generation or extraction, then demonstrate a small Foundry workflow with an authorized model and observable output. Before relying on independent AI-901 practice questions, check whether their explanations match the April 15, 2026 Microsoft objectives and distinguish conceptual answers from actual supported SDK or portal procedures. Third-party questions are preparation material, not official Microsoft registration or evidence that a cloud exercise has run.
Microsoft’s current AI-901 study guide lists the measured skills as of April 15, 2026. Use that specific version to map your preparation. Microsoft describes this as a fundamentals exam for someone at the beginning of AI solution development who understands basic Python syntax and Azure resources. It is neither a professional-level machine-learning research examination nor a promise that memorizing product names is enough to build a working application.
What changed from AI-900 to AI-901?
AI-900 was associated with the same Azure AI Fundamentals credential, but it covered an older mix of service concepts. AI-901 became its replacement as AI-900 retired at the end of June 2026. The credential name is still Microsoft Certified: Azure AI Fundamentals; candidates should select the active exam code on Microsoft’s official registration page rather than buy a study guide merely because it carries that credential name.
A major shift is the emphasis on Microsoft Foundry as the context for basic implementation. Someone studying only the definitions of sentiment analysis, classification, speech recognition and object detection may recognize these workloads yet still struggle to select a model deployment, configure a prompt, invoke a client or test a single agent. Conversely, someone who can follow a Foundry tutorial but cannot distinguish fairness from transparency or transcription from synthesis also has a gap.
Do not assume every AI-900 practice question is current AI-901 preparation. Older questions might still teach valid AI concepts, but they cannot substitute for the updated exam blueprint. A useful conversion exercise is to label an older topic still relevant, partially relevant, or legacy-only after comparing it with the official AI-901 skill statements. This is especially important where Azure service names, endpoints and supported features have changed.
Understand the two scored objective groups
The official guide assigns 40–45% of scored skills to Identify AI concepts and capabilities. This group includes responsible AI principles, the purpose and capabilities of models, deployment choices and examples of generative, agentic, language, speech, vision and extraction workloads. It asks candidates to make sound introductory decisions. For example, if a help desk needs to extract an order number from a sentence, entity extraction is a different workload from summarizing a long report.
The remaining 55–60% is Implement AI solutions by using Microsoft Foundry. This group includes system and user prompts, deployment and interaction with a model in Foundry, a lightweight Foundry SDK client, a simple single-agent solution and basic applications for text, speech, vision, image generation and Content Understanding. This larger share is why hands-on examples are worth studying even at fundamentals level. You do not need to design an enterprise-scale agent platform to demonstrate that you understand how a basic agent is created and tested.
Percentages are ranges, not a promise about the exact count of questions you’ll see from any topic. Do not calculate a precise question quota from them. Use the weighting to allocate study effort, while ensuring that no measured skill is completely neglected.
Build a concepts-first foundation without stopping there
Begin with task selection. Write a short example for each workload: predict a number, classify a record, extract an entity, recognize speech, generate a summary, interpret an image and return a response from a single agent. For each, identify the input, expected output and one way it can fail. This habit prevents confusing model sophistication with product fitness. A generative model is not always the best answer to a deterministic numerical check.
Next learn the six responsible AI themes used in the exam: fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability. Tie each to a concrete question. Can a user correct a mistaken decision? Is the application usable through a screen reader? What happens when an AI-produced result is unsupported? Which person owns an escalation? These scenarios are more memorable than abstract lists.
Finally learn deployment vocabulary: a model’s capabilities, context and output limits, supported modality, configuration choices, region and the difference between a model in a catalog and one made available to an application. Treat preview features carefully. Being visible in a portal or quickstart does not necessarily mean a feature is generally available in every region.
Practice the Foundry steps as a small workflow
Use an Azure subscription and a Microsoft Foundry project available to you. Confirm you have the necessary permissions, an eligible model and the correct project endpoint before trying an SDK example. Inside the portal, select a supported model, check its deployment options, deploy it if required and test a task with separate system and user messages. Record which instructions are meant to define behavior and which text represents user-provided data.
After that, build a minimal client that submits a request and inspects the model response. Add one validation rule, such as checking that a returned category is on an approved list, and a clear error state for a rejected or timed-out request. The goal is not to write many lines of Python. The goal is to understand the sequence: credentials → project/model endpoint → request → response → validation.
Create a single prompt agent next. Give it a narrow purpose, test an ordinary request, a request outside its scope and a follow-up turn. An agent is not inherently a safer or more accurate model; it is a way to package instructions, a model and potentially tools with the interaction. For a beginner exercise, keep the tool set narrow and do not let an experimental agent modify real accounts or customer records.
Study language, speech, vision and extraction as decisions
For text, contrast sentiment, entity recognition, keywords and summarization. For speech, explain recognition versus synthesis and when a spoken prompt can be handled by a multimodal model instead of a dedicated transcription pipeline. For vision, distinguish understanding existing visual content from generating a new image. For extraction, understand how Content Understanding can turn a form, document, image, audio clip or video into typed fields supported by source evidence.
These skills overlap, but they are not the same. An application that reads text from a photo may use OCR or document extraction; a model that answers questions about a picture interprets its semantics; a model that creates a picture generates new content. Consider whether the task needs a faithful source-derived value or a plausible creative output. This distinction helps you spot misleading answer choices.
Advanced Microsoft AI-103 articles can be relevant follow-up reading on Foundry deployments and identity, but they represent more engineering depth than AI-901 requires. Read them for a concrete example only after you understand the simpler fundamentals task, rather than copying the AI-103 objective list as your AI-901 study plan.
Create a practical five-session revision schedule
In session one, cover responsible AI and workload identification; explain each concept using your own fictional scenario. Session two covers models, capabilities, deployment options and prompt design. Session three covers the Foundry portal, a small SDK chat client and single-agent testing. Session four covers text, speech and vision, with emphasis on choosing the right task and handling uncertain outputs. Session five covers Content Understanding plus mixed questions requiring you to select an approach and explain its limits.
Give more time to the areas in which your practice results reveal actual weaknesses, but do not neglect the implementing half of the blueprint. A productive review question is not merely “What is named entity recognition?” but “A customer sends a complaint containing a product number, sentiment and a request for help: which outputs are needed, and what should a basic application verify before using them?”
A useful final check is to ask whether each practice activity corresponds to an explicit measured skill. A memorized list of service names might be relevant to the concepts section but does not demonstrate that a user can test an agent or extract a field. Conversely, a long technical tutorial might cover excellent engineering while going far beyond a fundamentals objective. Make a simple checklist beside the official guide, mark what you can explain without notes, and identify what you have actually tried in an authorized sandbox. Distinguish an experiment you ran from a capability you only read about; the two kinds of confidence are different.
Check the actual release and registration facts
Verify the official AI-901 exam page when you register. Microsoft’s page lists the exam, related certification, supported languages, current retirement information and a passing score of 700. Dates, price by country, language availability and scheduling can change; a screenshot or an older third-party page is not a reliable final authority. The study guide also notes that localized objective updates can arrive after the English version.
Success in AI-901 preparation means being able to explain a sensible AI design and trace a modest implementation in Microsoft Foundry. Keep the active exam objectives beside your notes, test what your application really does and identify where outputs may be wrong, unsafe or unavailable. That approach remains useful beyond the certification, because it teaches the difference between remembering an AI feature and responsibly using it.