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Microsoft · Fundamentals · AI-901

AI-901 Practice Exams & Study Guide

226 original practice questions across both AI-901 domains — every answer explained and backed by official Microsoft documentation. No dumps, no recycled braindumps — every answer is explained and linked to the exact official page behind it, so you learn why it's right.

Try 16 free questions Already own it? Start practicing

226

original questions

drag-and-drop & hotspot items

100%

questions with an official source link

Coverage follows the official AI-901 blueprint

Official domain	Exam weight	Our questions
Identify AI concepts and capabilities	40–45%	105
Implement AI solutions by using Microsoft Foundry	55–60%	121

Weights from the official Skills Measured outline (2026-07-01). Question counts follow the blueprint — never padded to hit a number.

Judge the quality yourself

Sample question 1

Before sharing support transcripts with an outside vendor, a company must automatically find and remove names, addresses, and phone numbers. Which text analysis capability does this require?

a.Detecting and redacting personally identifiable information (PII)✓ correct
b.Summarizing the transcripts into shorter versions
c.Detecting which language each transcript is written in
d.Synthesizing speech from the transcripts

Why A: (A) Finding and removing private details like names, addresses, and phone numbers is PII detection and redaction. It's a specialized form of entity detection, used to meet privacy requirements. (B) summarization shortens text but doesn't strip out private data, (C) language detection only identifies the language, and (D) speech synthesis produces audio, which is unrelated.

Sample question 2

A company builds an assistant that first retrieves relevant sections from its internal expenses policy and adds them to the prompt, so the model answers from that policy instead of giving a generic reply. Which technique is this?

a.Tokenization
b.Speech synthesis
c.Positional encoding
d.Retrieval augmented generation (RAG)✓ correct

Why D: (D) Retrieving relevant documents and adding them to the prompt so the response is grounded in that information is retrieval augmented generation. (A) tokenization and (C) positional encoding are internal steps in how a model processes text, and (B) speech synthesis produces audio. None of them grounds a response in retrieved content.

Every explanation names why the right answer is right and what each wrong option actually refers to — that's the standard across all 226 questions. Try 16 of them free →

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Practice Exams

$10.90launch price · 12-month access

✓ 226 original questions across all 2 official domains
✓ All four exam formats: single, multi-select, drag-and-drop, hotspot
✓ Exam, Review and Section modes with a weak-area report
✓ Every answer explained and linked to the official Microsoft page behind it

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Complete Bundle

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✓ Everything in Practice Exams
✓ The designed AI-901 Study Guide (PDF), taught in learning order
✓ The Quick Recap card deck for last-mile review
✓ One purchase, complete preparation
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Study Guide + Quick Recap

$6.90launch price · 12-month access

✓ Designed AI-901 Study Guide (PDF) with original diagrams
✓ Quick Recap card deck ending in a cram sheet
✓ Plain-English teaching around precise exam terms
See what's inside ↓

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Look inside the AI-901 Study Guide

The real opening of Chapter 1 — how the whole guide teaches. The full guide continues like this, chapter by chapter, with original diagrams and verified questions woven in.

Chapter 1 · free excerpt

What AI Is — and the Five Things It Does

The whole exam depends on one skill: hearing a real-world task and knowing which kind of AI it calls for. Before any Microsoft Foundry mechanics, we draw the map — what artificial intelligence actually is, and the five workload families you'll meet again and again. Every later chapter takes one of these families and shows how you build it. This chapter is the map, and we will return to it often.

Artificial intelligence is software that does things we normally associate with human intelligence. That means understanding what someone wrote or said, making sense of a picture, pulling facts out of a messy document, or producing brand-new content. For a fundamentals exam the goal isn't the mathematics underneath. The goal is a feel for the kinds of tasks AI can take on, and the vocabulary to name them.

Underneath almost every one of those capabilities sits machine learning. Rather than being programmed with explicit rules, a model is trained on large amounts of example data. Training continues until the model can make useful predictions about new input it has never seen. A vision model learns from many labelled images; a language model learns from vast quantities of text. You don't need the training details for this exam — just the idea that an AI capability is a trained model applied to fresh input.

Mental model

A useful way to remember the whole exam: think of AI as a set of senses plus a voice. It can see (computer vision), hear (speech), read and understand (natural language processing), pull the facts out of the clutter (information extraction), and speak and create (generative AI). Five abilities — that's the map.

Figure 1.1 — The five AI workloads in one view. Each family is defined by what it takes in and what it produces. Listen for the right-hand cue in an exam scenario — the input or the goal usually names the workload for you.

Worked example

Walk a mixed scenario the way the exam wants you to — splitting one product into its workloads. A travel company builds a help line. A caller can ask a question out loud (that's speech — recognition). The call is transcribed for the record (speech again — speech-to-text). The transcript is scored for how happy the caller sounded (NLP — sentiment analysis). The assistant drafts a friendly reply to read back (generative AI). And a photo of the customer's paper ticket is used to pull out the booking reference (information extraction). One feature, four workloads — and naming each one is exactly the skill Domain 1 tests.

Excerpt ends here — the full chapter continues with the five workloads in depth, exam traps, and verified practice questions.

Quick Recap — two of the AI-901 cards

A landscape card deck for last-mile review, ending in a Cram Sheet. One chapter card and one of the four Cram Sheet cards:

Chapter card

The five AI workloads

Every question starts here — name the family the task belongs to.

Cram sheet · 1 of 4

The five workloads, in one breath

Generative AI & agents create and converse · Computer vision reads images and video · Speech takes audio in and out · Natural language processing makes sense of written text · Information extraction turns documents and media into structured data.

What the designed PDFs look like

Real pages from the files you download — the polish is part of what you're paying for.

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