MS-100 Microsoft 365 Identity and Services

• Describe core concepts of machine learning
• Identify different types of machine learning
• Describe considerations for training and evaluating machine learning models
• Describe core concepts of deep learning
• Use automated machine learning in Azure Machine Learning service
• Understand applications Azure AI services can be used to build
• Understand how to access Azure AI services in the Azure portal
• Understand how to use Azure AI services keys and endpoint for authentication
• Create and use an Azure AI services resource in a Content Safety Studio setting
• Learn how to use the Azure AI Vision service to analyze images.
• Learn how to use Azure AI Face service to detect and analyze faces in images.
• Learn how to read text in images with Azure AI Vision.
• Learn how to use Azure AI Language for text analysis
• After completing this module, you’ll be able to understand how to use Azure AI Language and Azure AI
• Bot Service to create a bot.
• Learn what conversational language understanding is.
• Learn about key features, such as intents and utterances.
• Build and publish a natural-language machine-learning model.
• Learn about speech recognition and synthesis
• Learn how to use Azure AI Speech
• Learn how to use the prebuilt receipt processing capabilities of Azure AI Document Intelligence.
• Explore Azure AI Search
• Create an Azure AI Search index
• Import data to the index
• Query the Azure AI Search index
• Understand generative AI’s place in the development of artificial intelligence
• Understand large language models and their role in intelligent applications
• Describe how Azure OpenAI supports intelligent application creation
• Describe examples of copilots and good prompts
• Describe Azure OpenAI workloads and access the Azure OpenAI Service
• Understand generative AI models
• Understand Azure OpenAI’s language, code, and image capabilities
• Understand Azure OpenAI’s responsible AI practices and limited access policies
• Describe an overall process for responsible generative AI solution development
• Identify and prioritize potential harms relevant to a generative AI solution
• Measure the presence of harms in a generative AI solution
• Mitigate harms in a generative AI solution
• Prepare to deploy and operate a generative AI solution responsibly

1. Fundamental AI Concepts

With AI, we can build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.

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2. Fundamentals of machine learning

Machine learning is the basis for most modern artificial intelligence solutions. A familiarity with the core concepts on which machine learning is based is an important foundation for understanding AI.

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3. Fundamentals of Azure AI services

In this module, you learn the fundamentals of how Azure AI services can be used to build applications.

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4. Fundamentals of Computer Vision

Azure AI Vision service enables software engineers to create intelligent solutions that extract information from
images; a common task in many artificial intelligence (AI) scenarios.

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5. Fundamentals of Facial Recognition

Face detection, analysis, and recognition are important capabilities for artificial intelligence (AI) solutions.
Azure AI Face service in Azure makes it easy integrate these capabilities into your applications.

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6. Fundamentals of optical character recognition

Optical character recognition (OCR) enables artificial intelligence (AI) systems to read text in images, enabling
applications to extract information from photographs, scanned documents, and other sources of digitized text.

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7. Fundamentals of Text Analysis with the Language Service

Explore Azure AI Language’s natural language processing (NLP) features, which include sentiment analysis,
key phrase extraction, named entity recognition, and language detection

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8. Fundamentals of question answering with the Language Service

Create a custom question answering knowledge base with Azure AI Language and create a bot with Azure AI
Bot Service that answers user questions.

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9. Fundamentals of conversational language understanding

In this module, we introduce you to conversational language understanding, and show how to create
applications that understand language with Azure AI Language

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10. Fundamentals of Azure AI Speech

Learn how to recognize and synthesize speech by using Azure AI Speech.

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11. Fundamentals of Azure AI Document Intelligence

Document processing is a common task in many business scenarios. Organizations can use Azure AI Document Intelligence to automate data extraction across document types, such as receipts, invoices, and more.

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12.  Fundamentals of Knowledge Mining and Azure AI Search

Use Azure AI Search to make your data searchable

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13. Fundamentals of Generative AI

In this module you’ll explore the way in which large language models (LLMs) enable AI applications and
services to generate original content based on natural language input. You’ll also learn how generative AI
enables the creation of AI-powered copilots that can assist humans in creative tasks.

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14. Fundamentals of Azure OpenAI Service

Get to know the connection between artificial intelligence (AI), Responsible AI, and text, code, and image
generation. Understand how you can use Azure OpenAI to build solutions against AI models within Azure.

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15. Fundamentals of Responsible Generative AI

Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation

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The Azure AI Fundamentals (AI-900T00) course is designed for anyone interested in learning about the types of solutions 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.

Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students
start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.

Specifically:

• Experience using computers and the internet.
• Interest in use cases for AI applications and machine learning models.
• A willingness to learn through hands-on exploration.

Skills Measured

• 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%)

• Career growth
• Broad Career opportunities
• Worldwide recognition from leaders
• Up-to Date technical skills
• Popular Certification Badges

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