AI-102: Azure AI Engineer Associate Apr - 2025
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