AI-102T00: Designing and Implementing a Microsoft Azure AI Solution

• Plan and manage an Azure AI solution (25–30%)

• Implement image and video processing solutions (15–20%)

• Implement natural language processing solutions (25–30%)

• Implement knowledge mining solutions (5–10%)

• Implement conversational AI solutions (15–20%)

• 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 Networks

• 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

• Attach a Cognitive Services account to a skillset

• Select and include built-in skills for documents

• Implement custom skills and include them in a skillset

• Implement incremental enrichment

• 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

Before attending this course, students must have:

• Knowledge of Microsoft Azure and ability to navigate the Azure portal

• Knowledge of either C# or Python

• Familiarity with JSON and REST programming semantics