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Miscrosoft Certified Azure AI Engineer Associate

Design and implement an Azure AI solution using Azure AI services, Azure AI Search, and Azure Open AI.

Skills Measured

  • Plan and manage an Azure AI solution (20–25%)
  • Implement generative AI solutions (15–20%)
  • Implement an agentic solution (5–10%)
  • Implement computer vision solutions (10–15%)
  • Implement natural language processing solutions (15–20%)
  • Implement knowledge mining and information extraction solutions (15–
    20%)

COURSE OUTLINE

Plan and manage an Azure AI solution (20–25%)
Select the appropriate Azure AI Foundry services
  • Select the appropriate service for a generative AI solution
  • Select the appropriate service for a computer vision solution
  • Select the appropriate service for a natural language processing solution
  • Select the appropriate service for a speech solution
  • Select the appropriate service for an information extraction solution
  • Select the appropriate service for a knowledge mining solution
  • Plan for a solution that meets Responsible AI principles
  • Create an Azure AI resource
  • Choose the appropriate AI models for your solution
  • Deploy AI models using the appropriate deployment options
  • Install and utilize the appropriate SDKs and APIs
  • Determine a default endpoint for a service
  • Integrate Azure AI Foundry Services into a continuous integration and continuous delivery (CI/CD) pipeline
  • Plan and implement a container deployment
  • Monitor an Azure AI resource
  • Manage costs for Azure AI Foundry Services
  • Manage and protect account keys
  • Manage authentication for an Azure AI Foundry Service resource
  • Implement content moderation solutions
  • Configure responsible AI insights, including content safety
  • Implement responsible AI, including content filters and blocklists
  • Prevent harmful behavior, including prompt shields and harm detection
  • Design a responsible AI governance framework
Implement generative AI solutions (15–20%)
Build generative AI solutions with Azure AI Foundry
  • Plan and prepare for a generative AI solution
  • Deploy a hub, project, and necessary resources with Azure AI Foundry
  • Deploy the appropriate generative AI model for your use case
  • Implement a prompt flow solution
  • Implement a RAG pattern by grounding a model in your data
  • Evaluate models and flows
  • Integrate your project into an application with Azure AI Foundry SDK
  • Utilize prompt templates in your generative AI solution
  • Provision an Azure OpenAI in Foundry Models resource
  • Select and deploy an Azure OpenAI model
  • Submit prompts to generate code and natural language responses
  • Use the DALL-E model to generate images
  • Integrate Azure OpenAI into your own application
  • Use large multimodal models in Azure OpenAI
  • Implement an Azure OpenAI Assistant
  • Configure parameters to control generative behavior
  • Configure model monitoring and diagnostic settings, including performance
    and resource consumption
  • Optimize and manage resources for deployment, including scalability and
    foundational model updates
  • Enable tracing and collect feedback
  • Implement model reflection
  • Deploy containers for use on local and edge devices
  • Implement orchestration of multiple generative AI models
  • Apply prompt engineering techniques to improve responses
  • Fine-tune an generative model
Implement an agentic solution (5–10%)
Create custom agents
  • Understand the role and use cases of an agent
  • Configure the necessary resources to build an agent
  • Create an agent with the Azure AI Foundry Agent Service
  • Implement complex agents with Semantic Kernel and Autogen
  • Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities
  • Test, optimize and deploy an agent
Implement computer vision solutions (10–15%)
Analyze images
  • Select visual features to meet image processing requirements
  • Detect objects in images and generate image tags
  • Include image analysis features in an image processing request
  • Interpret image processing responses
  • Extract text from images using Azure AI Vision
  • Convert handwritten text using Azure AI Vision
  • Choose between image classification and object detection models
  • Label images
  • Train a custom image model, including image classification and object
    detection
  • Evaluate custom vision model metrics
  • Publish a custom vision model
  • Consume a custom vision model
  • Build a custom vision model code first
  • Use Azure AI Video Indexer to extract insights from a video or live stream
  • Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video
Implement natural language processing solutions (15–20%)
Analyze and translate text
  • Extract key phrases and entities
  • Determine sentiment of text
  • Detect the language used in text
  • Detect personally identifiable information (PII) in text
  • Translate text and documents by using the Azure AI Translator service
  • Integrate generative AI speaking capabilities in an application
  • Implement text-to-speech and speech-to-text using Azure AI Speech
  • Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
  • Implement custom speech solutions with Azure AI Speech
  • Implement intent and keyword recognition with Azure AI Speech
  • Translate speech-to-speech and speech-to-text by using the Azure AI Speech service
  • Create intents, entities, and add utterances
  • Train, evaluate, deploy, and test a language understanding model
  • Optimize, backup, and recover language understanding model
  • Consume a language model from a client application
  • Create a custom question answering project
  • Add question-and-answer pairs and import sources for question answering
  • Train, test, and publish a knowledge base
  • Create a multi-turn conversation
  • Add alternate phrasing and chit-chat to a knowledge base
  • Export a knowledge base
  • Create a multi-language question answering solution
  • Implement custom translation, including training, improving, and publishing a custom model
Implement knowledge mining and information extraction solutions (15–20%)
Implement an Azure AI Search solution
  • Provision an Azure AI Search resource, create an index, and define a skillset
  • Create data sources and indexers
  • Implement custom skills and include them in a skillset
  • Create and run an indexer
  • Query an index, including syntax, sorting, filtering, and wildcards
  • Manage Knowledge Store projections, including file, object, and table projections
  • Implement semantic and vector store solutions
  • Provision a Document Intelligence resource
  • Use prebuilt models to extract data from documents
  • Implement a custom document intelligence model
  • Train, test, and publish a custom document intelligence model
  • Create a composed document intelligence model
  • Create an OCR pipeline to extract text from images and documents
  • Summarize, classify, and detect attributes of documents
  • Extract entities, tables, and images from documents
  • Process and ingest documents, images, videos, and audio with Azure AI Content Understanding