| 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
Extract information with Azure Content Understanding in Foundry Tools
- 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 Content Understanding in Foundry Tools
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| 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 Translator in Foundry Tools service
Process and translate speech
- Integrate generative AI speaking capabilities in an application
- Implement text-to-speech and speech-to-text using Azure Speech in Foundry Tools
- Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
- Implement custom speech solutions with Azure Speech in Foundry Tools
- Implement intent and keyword recognition with Azure Speech in Foundry Tools
- Translate speech-to-speech and speech-to-text by using the Azure Speech in Foundry Tools 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 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 Vision in Foundry Tools
- Convert handwritten text using Azure Vision in Foundry Tools
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
- Use Azure AI Video Indexer to extract insights from a video or live stream
- Use Azure Vision in Foundry Tools Spatial Analysis to detect presence and movement of people in video
|
| 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 Microsoft Foundry Agent Service
- Implement complex agents with Microsoft Agent Framework
- Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities
- Test, optimize and deploy an agent
|
| Implement generative AI solutions |
15-20% |
Build generative AI solutions with Microsoft Foundry
- Plan and prepare for a generative AI solution
- Deploy a hub, project, and necessary resources with Microsoft 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 Microsoft Foundry SDK
- Utilize prompt templates in your generative AI solution
Use Azure OpenAI in Foundry Models to generate content
- 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
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 a generative model
|
| Plan and manage an Azure AI solution |
20-25% |
Select the appropriate Microsoft 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, create and deploy a Microsoft Foundry 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 Microsoft Foundry Services into a continuous integration and continuous delivery (CI/CD) pipeline
- Plan and implement a container deployment
Manage, monitor, and secure a Microsoft Foundry Service
- Monitor an Azure AI resource
- Manage costs for Microsoft Foundry Services
- Manage and protect account keys
- Manage authentication for a Microsoft Foundry 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
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| Official Information |
|
https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102 |
