Microsoft AI-901 Exam Syllabus

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Before starting your AI-901 exam preparation, it is recommended to review the complete Microsoft Azure AI Fundamentals (Updated Version) exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free AI-901 questions. We also provide premium AI-901 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Microsoft AI-901 Exam Objectives

Section	Weight	Objectives
Identify AI concepts and capabilities	40-45%	Describe principles of responsible AI Describe considerations for fairness in an AI solution Describe considerations for reliability and safety in an AI solution Describe considerations for privacy and security in an AI solution Describe considerations for inclusiveness in an AI solution Describe considerations for transparency in an AI solution Describe considerations for accountability in an AI solution Identify AI model components and configurations Describe how generative AI models work Identify an appropriate AI model, based on capabilities Identify appropriate model deployment options and configuration parameters Identify AI workloads Identify scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction Describe common text analysis techniques, including keyword extraction, entity detection, sentiment analysis, and summarization Identify features and capabilities of speech recognition and speech synthesis Identify features and capabilities of computer vision and image-generation models Identify techniques to extract information from text, images, audio, and videos
Implement AI solutions by using Microsoft Foundry	55-60%	Implement generative AI apps and agents by using Foundry Create effective system and user prompts for generative AI models Deploy a model and interact with it in the Foundry portal Create a lightweight chat client application by using the Foundry SDK Create and test a single-agent solution in the Foundry portal Create a lightweight client application for an agent Implement AI solutions for text and speech by using Foundry Build a lightweight application that includes text analysis Respond to spoken prompts by using a deployed multimodal model Build a lightweight application by using Azure Speech in Foundry Tools Implement AI solutions with computer vision and image-generation capabilities by using Foundry Interpret visual input in prompts by using a deployed multimodal model Create new visual outputs by using generative models Build a lightweight application that includes vision capabilities Implement AI solutions for information extraction by using Foundry Extract information from documents and forms by using Azure Content Understanding in Foundry Tools Extract information from images by using Content Understanding Extract information from audio and video by using Content Understanding Build a lightweight application with information extraction capabilities by using Content Understanding
Official Information		https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901

Section

Weight

Objectives

Identify AI concepts and capabilities

40-45%

Describe principles of responsible AI

Describe considerations for fairness in an AI solution
Describe considerations for reliability and safety in an AI solution
Describe considerations for privacy and security in an AI solution
Describe considerations for inclusiveness in an AI solution
Describe considerations for transparency in an AI solution
Describe considerations for accountability in an AI solution

Identify AI model components and configurations

Describe how generative AI models work
Identify an appropriate AI model, based on capabilities
Identify appropriate model deployment options and configuration parameters

Identify AI workloads

Identify scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
Describe common text analysis techniques, including keyword extraction, entity detection, sentiment analysis, and summarization
Identify features and capabilities of speech recognition and speech synthesis
Identify features and capabilities of computer vision and image-generation models
Identify techniques to extract information from text, images, audio, and videos

Implement AI solutions by using Microsoft Foundry

55-60%

Implement generative AI apps and agents by using Foundry

Create effective system and user prompts for generative AI models
Deploy a model and interact with it in the Foundry portal
Create a lightweight chat client application by using the Foundry SDK
Create and test a single-agent solution in the Foundry portal
Create a lightweight client application for an agent

Implement AI solutions for text and speech by using Foundry

Build a lightweight application that includes text analysis
Respond to spoken prompts by using a deployed multimodal model
Build a lightweight application by using Azure Speech in Foundry Tools

Implement AI solutions with computer vision and image-generation capabilities by using Foundry

Interpret visual input in prompts by using a deployed multimodal model
Create new visual outputs by using generative models
Build a lightweight application that includes vision capabilities

Implement AI solutions for information extraction by using Foundry

Extract information from documents and forms by using Azure Content Understanding in Foundry Tools
Extract information from images by using Content Understanding
Extract information from audio and video by using Content Understanding
Build a lightweight application with information extraction capabilities by using Content Understanding

Official Information

https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901

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