AI 901 Microsoft Azure AI Fundamentals Practice Test

Pass the Microsoft AI-901 Azure AI Fundamentals certification with a complete exam prep course, including 324 practice tests and exam-style questions covering AI workloads, responsible AI principles, generative AI, machine learning, and Microsoft Foundry.

Prepare for the Microsoft AI-901: Azure AI Fundamentals Certification Exam with a comprehensive course designed to help you master Microsoft Azure AI concepts and successfully pass the AI-901 certification.

Whether you are a technical beginner or an aspiring AI developer looking to validate your foundational AI skills, this course will help you learn core Microsoft AI services, generative AI and agents, responsible AI frameworks, basic Python syntax, and AI application building using Microsoft Foundry.

The course is strictly aligned with the newly updated official Microsoft AI-901 exam syllabus, ensuring you are fully prepared for the 2026 certification exam.

AI-901 Exam Domains Covered

This course follows the official Azure AI Fundamentals certification AI-901 exam skills outline, ensuring complete coverage of the newly structured certification exam topics. The AI-901 exam measures your knowledge across two major domains:

Identify AI concepts and responsibilities (40–45%)
Implement AI solutions by using Microsoft Foundry (55–60%)

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

Happy Learning

Team Codaming