AI-102: Microsoft Azure AI Engineer Associate May - 2025 Free Coupon

Course Description

Skills at a glance

• 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%)

Plan and manage an Azure AI solution (20–25%)

Select the appropriate Azure AI 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, create and deploy an Azure AI service

• 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 services into a continuous integration and continuous delivery (CI/CD) pipeline

• Plan and implement a container deployment

Manage, monitor, and secure an Azure AI service

• Monitor an Azure AI resource

• Manage costs for Azure AI services

• Manage and protect account keys

• Manage authentication for an Azure AI Service resource

Implement AI solutions responsibly

• 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

Use Azure OpenAI Service to generate content

• Provision an Azure OpenAI Service 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

Optimize and operationalize a generative AI solution

• 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 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

Implement custom vision models

• 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

Analyze videos

• 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

Process and translate speech

• 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

Implement custom language models

• 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

Implement an Azure AI Document Intelligence solution

• 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

Extract information with Azure AI Content Understanding

• 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