AI-102 Microsoft Azure AI Solution Practice Tests Exam Prep

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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution 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, Text Analytics, 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 APIExtract 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 APIImplement 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 Speechservice 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 Text Analytics 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 an initial language model by using Language Understanding Service (LUIS) create intents and entities based on a schema, and then add utterances create complex hierarchical entitieso use this instead of roles train and deploy a model Iterate on and optimize a language model by using LUIS 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 LUIS 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 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%)Create a knowledge base by using Qn A Maker create a Qn A Maker service create a knowledge base import a knowledge base 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 manage collaborators 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 anddialogs 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 Qn A Maker service integrate a LUIS service integrate a Speech service integrate Orchestrator for multiple language models manage keys in app settings file