AI-901 Exam Blueprint: Azure AI Fundamentals
Map the current AI-901 domains to concept checks and lightweight Foundry implementation practice.
Use IT Mastery to practice both AI concepts and lightweight application decisions. This blueprint map helps you choose what to review next; it follows the current AI-901 scope rather than an older Azure AI service checklist.
Current domain weights
| Official domain | Microsoft weighting | Preparation focus |
|---|---|---|
| Identify AI concepts and capabilities | 40–45% | Explain model behavior, responsible AI, and the differences between AI workloads. |
| Implement AI solutions by using Microsoft Foundry | 55–60% | Read and configure small applications for models, agents, text, speech, visual content, and extraction. |
Official source check — September 9, 2026: these are the ranges in Microsoft’s AI-901 study guide , under skills measured as of April 15, 2026. Microsoft can update the outline; check it again when planning your exam.
Turn the scope into practice decisions
| Area | Check your understanding | Useful practice task |
|---|---|---|
| Responsible AI | Distinguish fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. | Match a specific risk to a safeguard that addresses it. |
| Models and settings | Explain tokens, embeddings, generation, model capabilities, deployment choices, and runtime settings. | Compare model evidence and constraints; distinguish an output-length limit from deployment capacity. |
| AI workloads | Separate generation, text analysis, speech, vision, and information extraction. | Identify the input, required output, and processing task before choosing a capability. |
| Generative apps and agents | Understand prompts, model calls, reusable agent instructions, tools, and conversation context. | Trace a small client request and decide whether it invokes a model or the intended saved agent. |
| Text and speech | Choose an analysis approach and interpret text or audio results. | Distinguish transcription from a spoken answer; inspect language-analysis results and redacted text. |
| Visual input and output | Distinguish interpreting an image from creating or modifying visual content. | Check that an application actually supplies the image and handles the returned result. |
| Information extraction | Connect source evidence, an analyzer, an output schema, and validation. | Preserve repeated records and their relationships when mapping extracted fields into an application. |
Include every extraction modality
Content Understanding preparation should include more than invoices. For a document, check named fields and repeated line items. For an image, identify the visual information the application needs as structured fields. For recorded audio, separate the transcript from requested details such as an issue and promised follow-up. For video, consider which event or segment supports the requested information.
In each case, practice matching the analyzer and output schema to the application requirement, then checking that the returned information is usable. Do not assume that every modality returns the same metadata or confidence fields.
Prepare for lightweight implementation
The candidate profile includes Python syntax and programming techniques, Azure resources, and familiarity with REST APIs, SDKs, and command-line tools. Review a short current sample and explain where input comes from, how the caller authenticates, which endpoint or deployment it uses, and how the application consumes the result.
When comparing a playground test with code, check the actual prompt, model target, supported settings, and conversation context. Changing a model does not repair an application that reads the wrong result field.
For asynchronous work, distinguish an accepted request from a completed operation. A job identifier is useful for checking progress; it is not the extracted document fields or finished video.
Keep review within AI-901 depth
Make Foundry applications and current multimodal workflows central to your preparation. Detailed custom-model training, ML pipelines, search-index administration, and multi-agent orchestration should not displace the current fundamentals outline.
Mark each area as explain, apply, or review. Move an area to “apply” only when you can justify an answer from the facts and explain why a plausible alternative fails a requirement. Use the study plan to turn those gaps into your next sessions.