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Prerequisites
Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
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Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
Microsoft AI-102 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Plan and Manage an Azure Cognitive Services Solution (15-20%) | |
| Select the appropriate Cognitive Services resource | - select the appropriate cognitive service for a vision solution - select the appropriate cognitive service for a language analysis solution - select the appropriate cognitive Service for a decision support solution - select the appropriate cognitive service for a speech solution |
| Plan and configure security for a Cognitive Services solution | - manage Cognitive Services account keys - manage authentication for a resource - secure Cognitive Services by using Azure Virtual Network - plan for a solution that meets responsible AI principles |
| Create a Cognitive Services resource | - create a Cognitive Services resource - configure diagnostic logging for a Cognitive Services resource - manage Cognitive Services costs - monitor a cognitive service - implement a privacy policy in Cognitive Services |
| Plan and implement Cognitive Services containers | - identify when to deploy to a container - containerize Cognitive Services (including Computer Vision API, Face API, Languages, Speech, Form Recognizer) - deploy Cognitive Services Containers in Microsoft Azure |
Implement Computer Vision Solutions (20-25%) | |
| Analyze images by using the Computer Vision API | - retrieve image descriptions and tags by using the Computer Vision API - identify landmarks and celebrities by using the Computer Vision API - detect brands in images by using the Computer Vision API - moderate content in images by using the Computer Vision API - generate thumbnails by using the Computer Vision API |
| Extract text from images | - extract text from images or PDFs by using the Computer Vision service - extract information using pre-built models in Form Recognizer - build and optimize a custom model for Form Recognizer |
| Extract facial information from images | - detect faces in an image by using the Face API - recognize faces in an image by using the Face API - analyze facial attributes by using the Face API - match similar faces by using the Face API |
| Implement image classification by using the Custom Vision service | - label images by using the Computer Vision Portal - train a custom image classification model in the Custom Vision Portal - train a custom image classification model by using the SDK - manage model iterations - evaluate classification model metrics - publish a trained iteration of a model - export a model in an appropriate format for a specific target - consume a classification model from a client application - deploy image classification custom models to containers |
| Implement an object detection solution by using the Custom Vision service | - label images with bounding boxes by using the Computer Vision Portal - train a custom object detection model by using the Custom Vision Portal - train a custom object detection model by using the SDK - manage model iterations - evaluate object detection model metrics - publish a trained iteration of a model - consume an object detection model from a client application - deploy custom object detection models to containers |
| Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer) | - process a video - extract insights from a video - moderate content in a video - customize the Brands model used by Video Indexer - customize the Language model used by Video Indexer by using the Custom Speech service - customize the Person model used by Video Indexer - extract insights from a live stream of video data |
Implement Natural Language Processing Solutions (20-25%) | |
| Analyze text by using the Language service | - retrieve and process key phrases - retrieve and process entity information (people, places, urls, etc.) - retrieve and process sentiment - detect the language used in text |
| Manage speech by using the Speech service | - implement text-to-speech - customize text-to-speech - implement speech-to-text - improve speech-to-text accuracy - improve text-to-speech accuracy - implement intent recognition |
| Translate language | - translate text by using the Translator service - translate speech-to-speech by using the Speech service - translate speech-to-text by using the Speech service |
| Build a initial language model by using Language Understanding Service (LUIS) | - create intents and entities based on a schema, and add utterances - create complex hierarchical entities
- train and deploy a model |
| Iterate on and optimize a language model by using Language Understanding | - implement phrase lists - implement a model as a feature (i.e. prebuilt entities) - manage punctuation and diacritics - implement active learning - monitor and correct data imbalances - implement patterns |
| Manage a Language Understanding model | - manage collaborators - manage versioning - publish a model through the portal or in a container - export a LUIS package - deploy a LUIS package to a container - integrate Bot Framework (LUDown) to run outside of the LUIS portal |
| Create a Questions Answering solution using the Language service | - create a question answering project - import questions and answers - 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 - add active learning to a knowledge base |
Implement Knowledge Mining Solutions (15-20%) | |
| Implement a Cognitive Search solution | - create data sources - define an index - create and run an indexer - query an index - configure an index to support autocomplete and autosuggest - boost results based on relevance - implement synonyms |
| Implement an enrichment pipeline | - 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 a knowledge store | - define file projections - define object projections - define table projections - query projections |
| Manage a Cognitive Search solution | - provision Cognitive Search - configure security for Cognitive Search - configure scalability for Cognitive Search |
| Manage indexing | - manage re-indexing - rebuild indexes - schedule indexing - monitor indexing - implement incremental indexing - manage concurrency - push data to an index - troubleshoot indexing for a pipeline |
Implement Conversational AI Solutions (15-20%) | |
| Design and implement conversation flow | - design conversation logic for a bot - create and evaluate *.chat file conversations by using the Bot Framework Emulator - choose an appropriate conversational model for a bot, including activity handlers and dialogs |
| Create a bot by using the Bot Framework SDK | - use the Bot Framework SDK to create a bot from a template - implement activity handlers and dialogs - use Turn Context - test a bot using the Bot Framework Emulator - deploy a bot to Azure |
| Create a bot by using the Bot Framework Composer | - implement dialogs - maintain state - implement logging for a bot conversation - implement prompts for user input - troubleshoot a conversational bot - test a bot - publish a bot - add language generation for a response - design and implement adaptive cards |
| Integrate Cognitive Services into a bot | - integrate a question answering model - integrate a LUIS service - integrate a Speech service resource |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage an Azure AI Solution | - Manage security, compliance, and responsible AI - Plan solution requirements and architecture - Select appropriate Azure AI services |
| Topic 2: Implement Natural Language Processing Solutions | - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services - Implement question answering and conversational AI |
| Topic 3: Implement Knowledge Mining and Document Intelligence | - Implement document intelligence solutions - Enrich and index data for search scenarios - Build Azure AI Search solutions |
| Topic 4: Implement Generative AI Solutions | - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions - Build retrieval-augmented generation (RAG) solutions |
| Topic 5: Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Implement custom vision models - Extract information using OCR and form recognition |
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