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The Designing and Implementing an Azure AI Solution AI-100 and AI-102
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Recommend Azure Cognitive Services APIs to meet business requirements
Design solutions that include one or more pipelines
Implement an AI workflow
- Select the appropriate service for a vision solution
- Select the appropriate service for a language analysis solution
- Select the appropriate service for a decision support solution
- Select the appropriate service for a speech solution
- Select the appropriate Applied AI services
- Manage account keys
- Manage authentication for a resource
- Secure services by using Azure Virtual Ne
tworks
- Plan for a solution that meets responsible AI principles
- Create an Azure AI resource
- Configure diagnostic logging
- Manage costs for Azure AI services
- Monitor an Azure AI resource
- Determine a default endpoint for a service
- Create a resource by using the Azure portal
- Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline
- Plan a container deployment
- Implement prebuilt containers in a connected environment
- Create a solution that uses Anomaly Detector, part of Cognitive Services
- Create a solution that uses Azure Content Moderator, part of Cognitive Services
- Create a solution that uses Personalizer, part of Cognitive Services
- Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services
- Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services
- Select appropriate visual features to meet image processing requirements
- Create an image processing request to include appropriate image analysis features
- Interpret image processing responses
- Extract text from images or PDFs by using the Computer Vision service
- Convert handwritten text by using the Computer Vision service
- Extract information using prebuilt models in Azure Form Recognizer
- Build and optimize a custom model for Azure Form Recognizer
- Choose between image classification and object detection models
- Specify model configuration options, including category, version, and compact
- Label images
- Train custom image models, including classifiers and detectors
- Manage training iterations
- Evaluate model metrics
- Publish a trained iteration of a model
- Export a model to run on a specific target
- Implement a Custom Vision model as a Docker container
- Interpret model responses
- Process a video by using Azure Video Indexer
- Extract insights from a video or live stream by using Azure Video Indexer
- Implement content moderation by using Azure Video Indexer
- Integrate a custom language model into Azure Video Indexer
- Retrieve and process key phrases
- Retrieve and process entities
- Retrieve and process sentiment
- Detect the language used in text
- Detect personally identifiable information (PII)
- Implement and customize text-to-speech
- Implement and customize speech-to-text
- Improve text-to-speech by using SSML and Custom Neural Voice
- Improve speech-to-text by using phrase lists and Custom Speech
- Implement intent recognition
- Implement keyword recognition
- Translate text and documents by using the Translator service
- Implement custom translation, including training, improving, and publishing a custom model
- Translate speech-to-speech by using the Speech service
- Translate speech-to-text by using the Speech service
- Translate to multiple languages simultaneously
- Create intents and add utterances
- Create entities
- Train evaluate, deploy, and test a language understanding model
- Optimize a Language Understanding (LUIS) model
- Integrate multiple language service models by using Orchestrator
- Import and export language understanding models
- Create a question answering project
- Add question-and-answer pairs manually
- Import sources
- Train and test a knowledge base
- Publish a knowledge base
- Create a multi-turn conversation
- Add alternate phrasing
- Add chit-chat to a knowledge base
- Export a knowledge base
- Create a multi-language question answering solution
- Create a multi-domain question answering solution
- Use metadata for question-and-answer pairs
- Provision a Cognitive Search resource
- Create data sources
- Define an index
- Create and run an indexer
- Query an index, including syntax, sorting, filtering, and wildcards
- Manage knowledge store projections, including file, object, and table projections
- Design conversational logic for a bot
- Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot
- Create a bot from a template
- Create a bot from scratch
- Implement activity handlers, dialogs or topics, and triggers
- Implement channel-specific logic
- Implement Adaptive Cards
- Implement multi-language support in a bot
- Implement multi-step conversations
- Manage state for a bot
- Integrate Cognitive Services into a bot, including question answering, language understanding, and Speech service
- Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app
- Test a bot in a channel-specific environment
- Troubleshoot a conversational bot
- Deploy bot logic
