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EXAM NUMBER : AI-900

EXAM NAME : Microsoft Azure AI Fundamentals

Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.





Candidates for the Azure AI Fundamentals certification should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.





This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.


This certification is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required;

however, some general programming knowledge or experience would be beneficial.





Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but its not a prerequisite for any of them.





Describe AI workloads and considerations


Describe fundamental principles of machine learning on Azure


Describe features of computer vision workloads on Azure


Describe features of Natural Language Processing (NLP) workloads on Azure


Describe features of conversational AI workloads on Azure





Module 1: Introduction to AI


In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development.





Artificial Intelligence in Azure


Responsible AI


After completing this module you will be able to:





Describe Artificial Intelligence workloads and considerations


Module 2: Machine Learning


Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.





Introduction to Machine Learning


Azure Machine Learning


After completing this module you will be able to:





Describe fundamental principles of machine learning on Azure


Module 3: Computer Vision


Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services.





Computer Vision Concepts


Computer Vision in Azure


After completing this module you will be able to:





Describe features of computer vision workloads on Azure


Module 4: Natural Language Processing


This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands.





After completing this module you will be able to:





Describe features of Natural Language Processing (NLP) workloads on Azure


Module 5: Conversational AI


Conversational AI enables users to engage in a dialog with an AI agent, or bot, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.





Conversational AI Concepts


Conversational AI in Azure


After completing this module you will be able to:





Describe features of conversational AI workloads on Azure

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