Microsoft Certified Azure AI Engineer Associate: AI 102

Microsoft Certified Azure AI Engineer Associate: AI-102

• Lesson 01: Prepare to develop AI solutions on Azure

  • Understand capabilities of Azure Machine Learning

  • Understand capabilities of Azure AI Services

  • Understand capabilities of Azure OpenAI Service

  • Understand capabilities of Azure AI Search

• Lesson 02: Create and consume Azure AI services

  • Create Azure AI services resources in an Azure subscription

  • Identify endpoints, keys, and locations

  • Use a REST API and an SDK to consume Azure AI services

• Lesson 03: Secure Azure AI services

  • Consider authentication for Azure AI services

  • Manage network security for Azure AI services

• Lesson 04: Monitor Azure AI services

  • Monitor Azure AI services costs

  • Create alerts and view metrics for Azure AI services

  • Manage Azure AI services diagnostic logging

• Lesson 05: Deploy Azure AI services in containers

  • Create containers for reuse

  • Deploy to a container and secure a container

  • Consume Azure AI services from a container

• Lesson 06: Analyze images

  • Provision an Azure AI Vision resource

  • Analyze an image

  • Generate a smart-cropped thumbnail

• Lesson 07: Classify images

  • Provision Azure resources for Azure AI Custom Vision

  • Understand image classification

  • Train an image classifier

• Lesson 08: Detect, analyze, and recognize faces

  • Identify options for face detection, analysis, and identification

  • Understand considerations for face analysis

  • Detect faces with the Computer Vision service

  • Understand capabilities of the Face service

  • Compare and match detected faces

  • Implement facial recognition

• Lesson 09: Read Text in images and documents with the Azure AI Vision Service

  • Read text from images using OCR

  • Use the Azure AI Vision service Image Analysis with SDKs

  • Develop an application that can read printed and handwritten text

• Lesson 10: Analyze video

  • Describe Azure Video Indexer capabilities

  • Extract custom insights

  • Use Azure Video Indexer widgets and APIs

• Lesson 11: Analyze text with Azure AI Language

  • Detect language from text

  • Analyze text sentiment

  • Extract key phrases, entities, and linked entities

• Lesson 12: Create question answering solutions with Azure AI Language

  • Understand question answering and how it compares to language understanding

  • Create, test, publish, and consume a knowledge base

  • Implement multi-turn conversation and active learning

  • Create a question answering bot to interact with using natural language

• Lesson 13: Build a conversational language understanding model

  • Provision Azure resources for Azure AI Language resource

  • Define intents, utterances, and entities

  • Use patterns to differentiate similar utterances

  • Use pre-built entity components

  • Train, test, publish, and review an Azure AI Language model

• Lesson 14: Create a custom text classification solution

  • Understand types of classification projects

  • Build a custom text classification project

  • Tag data, train, and deploy a model

  • Submit classification tasks from your own app

• Lesson 15: Custom named entity recognition

  • Understand tagging entities in extraction projects

  • Understand how to build entity recognition projects

• Lesson 16: Translate text with Azure AI Translator service

  • Provision a Translator resource

  • Understand language detection, translation, and transliteration

  • Specify translation options

  • Define custom translations

• Lesson 17: Create speech-enabled apps with Azure AI services

  • Provision an Azure resource for the Azure AI Speech service

  • Use the Azure AI Speech to text API to implement speech recognition

  • Use the Text to speech API to implement speech synthesis

  • Configure audio format and voices

  • Use Speech Synthesis Markup Language (SSML)

• Lesson 18: Translate speech with the Azure AI Speech service

  • Provision Azure resources for speech translation

  • Generate text translation from speech

  • Synthesize spoken translations

• Lesson 19: Create an Azure AI Search solution

  • Create an Azure AI Search solution

  • Develop a search application

• Lesson 20: Create a custom skill for Azure AI Search

  • Implement a custom skill for Azure AI Search

  • Integrate a custom skill into an Azure AI Search skillset

• Lesson 21: Create a knowledge store with Azure AI Search

  • Create a knowledge store from an Azure AI Search pipeline

  • View data in projections in a knowledge store

• Lesson 22: Plan an Azure AI Document Intelligence solution

  • Describe Azure AI Document Intelligence solution components

  • Create and connect to Azure AI Document Intelligence resources

  • Choose whether to use a prebuilt, custom, or composed model

• Lesson 23: Use prebuilt Document intelligence models

  • Identify business problems by using prebuilt models in Forms Analyzer

  • Analyze forms by using the General Document, Read, and Layout models

  • Analyze forms by using financial, ID, and tax prebuilt models

• Lesson 24: Extract data from forms with Azure Document intelligence

  • Identify how Document intelligence's service, and models can automate processes

  • Use Document intelligence's capabilities with SDKs, and REST API

  • Develop and test custom models

• Lesson 25: Get started with Azure OpenAI Service

  • Create an Azure OpenAI Service resource

  • Deploy a base model and test it in the Studio's playgrounds

  • Generate completions to prompts and begin to manage model parameters

• Lesson 26: Build natural language solutions with Azure OpenAI Service

  • Integrate Azure OpenAI into your application

  • Differentiate between different endpoints available to your application

  • Generate completions to prompts using the REST API

• Lesson 27: Apply prompt engineering with Azure OpenAI Service

  • Understand the concept of prompt engineering

  • Know how to design and optimize prompts to better utilize AI models

• Lesson 28: Generate code with Azure OpenAI Service

  • Use natural language prompts to write code

  • Build unit tests and understand complex code with AI models

  • Generate comments and documentation for existing code

• Lesson 29: Generate images with Azure OpenAI Service

  • Describe the capabilities of DALL-E in the Azure openAI service

  • Use the DALL-E playground in Azure OpenAI Studio

  • Integrate DALL-E image generation into your apps

• Lesson 30: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

  • Describe the capabilities of Azure OpenAI on your data

  • Configure Azure OpenAI to use your own data

  • Use Azure OpenAI API to generate responses based on your own data

• Lesson 31: Fundamentals of Responsible Generative AI

  • 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