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Microsoft Certification Training

AI-900: Microsoft Azure AI Fundamentals

Prepare to pass the AI-900: Microsoft Azure AI Fundamentals Certification Exam

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Course Description

This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training . The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.

Audience Profile

The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.

Course Syllabus

What Will You Learn?

Course Description

Skills at a glance

  • Describe Artificial Intelligence workloads and considerations (15–20%)

  • Describe fundamental principles of machine learning on Azure (20–25%)

  • Describe features of computer vision workloads on Azure (15–20%)

  • Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

  • Describe features of generative AI workloads on Azure (15–20%)

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

  • Identify features of content moderation and personalization workloads

  • Identify computer vision workloads

  • Identify natural language processing workloads

  • Identify knowledge mining workloads

  • Identify document intelligence workloads

  • Identify features of generative AI workloads

Identify guiding principles for 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

Describe fundamental principles of machine learning on Azure (20–25%)

Identify common machine learning techniques

  • Identify regression machine learning scenarios

  • Identify classification machine learning scenarios

  • Identify clustering machine learning scenarios

  • Identify features of deep learning techniques

Describe core machine learning concepts

  • Identify features and labels in a dataset for machine learning

  • Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

  • Describe capabilities of automated machine learning

  • Describe data and compute services for data science and machine learning

  • Describe model management and deployment capabilities in Azure Machine Learning

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution

  • Identify features of image classification solutions

  • Identify features of object detection solutions

  • Identify features of optical character recognition solutions

  • Identify features of facial detection and facial analysis solutions

Identify Azure tools and services for computer vision tasks

  • Describe capabilities of the Azure AI Vision service

  • Describe capabilities of the Azure AI Face detection service

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP Workload Scenarios

  • Identify features and uses for key phrase extraction

  • Identify features and uses for entity recognition

  • Identify features and uses for sentiment analysis

  • Identify features and uses for language modeling

  • Identify features and uses for speech recognition and synthesis

  • Identify features and uses for translation

Identify Azure tools and services for NLP workloads

  • Describe capabilities of the Azure AI Language service

  • Describe capabilities of the Azure AI Speech service

Describe features of generative AI workloads on Azure (15–20%)

Identify features of generative AI solutions

  • Identify features of generative AI models

  • Identify common scenarios for generative AI

  • Identify responsible AI considerations for generative AI

Identify capabilities of Azure OpenAI Service

  • Describe natural language generation capabilities of Azure OpenAI Service

  • Describe code generation capabilities of Azure OpenAI Service

  • Describe image generation capabilities of Azure OpenAI Service

 

Duration

1 Day

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