Microsoft Azure AI Fundamentals Study Guide

How the Exam Is Structured

Microsoft Azure AI Fundamentals (AI-900) validates describe ai workloads and considerations, describe fundamental principles of machine learning on azure, describe azure ai services for computer vision and document processing, describe azure ai services for natural language processing and speech. The ExamPal practice bank includes 291 premium questions and 40 free questions mapped across the official blueprint.

15% of exam

Domain 2: Describe fundamental principles of machine learning on Azure

Covers core machine learning workloads, the machine learning process, Azure Machine Learning capabilities, common metrics, and factors affecting model quality and reliability. This domain focuses on foundational ML concepts as implemented on Azure.

20% of exam

Domain 4: Describe Azure AI services for natural language processing and speech

Covers text analysis, question answering, conversational language understanding, speech service capabilities, and choosing the right language or speech capability for a scenario. This domain focuses on language and voice AI services on Azure.

0% of exam

Domain 6: Apply AI-900 knowledge to scenario-based solution selection

Covers scenario-based selection of Azure AI capabilities, responsible AI implications in business scenarios, and interpreting common exam-style questions and distractors. This domain is focused on applying knowledge rather than learning new service features.

Key Terms to Know

These terms are loaded from the shared terminology pack and appear across the question explanations.

AI Impact Assessment

A structured evaluation used to document an AI system’s purpose, expected use, risks, and potential harms.

Accountability

A responsible AI principle requiring that AI systems be subject to human oversight, audit, and review.

Auditability

The ability to examine and review an AI system’s behavior, decisions, and processes.

Azure AI Question Answering

An Azure AI service used to create knowledge bases from FAQs, documents, and other content to answer user questions.

Azure AI Search

An Azure service for indexing, searching, and retrieving information from structured and unstructured content.

Azure Bot Service

An Azure service for building, deploying, and managing chatbots that interact with users through conversational interfaces.

Azure OpenAI Service

Microsoft’s managed Azure service for deploying, hosting, and using OpenAI generative AI models.

Chatbot

A software application that simulates conversation with users through text or voice interactions.

Client library

A language-specific software package that simplifies calling and integrating with a service API.

Computer vision

An AI field and Azure capability focused on extracting meaning and insights from images and visual data.

Content filters

Safety mechanisms that detect, block, or flag harmful or unsafe prompts and model outputs.

Conversational AI

AI systems designed to support natural dialogue between humans and machines using language understanding and response generation.

Data source

An external repository or storage location from which data is retrieved for processing or indexing.

Entity

A specific piece of information extracted from text, such as a date, location, or product name.

FAQ document

A document containing frequently asked questions and answers that can be imported into a knowledge base.

Feature

An input variable or attribute used by a machine learning model to make predictions.

GPT

A family of generative pretrained transformer models used for language tasks such as summarization and text generation.

Generative AI

AI systems that create new content such as text, images, or code from prompts or input data.