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AI-900 Exam Format | AI-900 Course Contents | AI-900 Course Outline | AI-900 Exam Syllabus | AI-900 Exam Objectives

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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