Microsoft AI-102 Exam Syllabus

Designing and Implementing a Microsoft Azure AI Solution

Last Update Nov 23, 2024
Total Questions : 307

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Microsoft AI-102 Exam Overview :

Exam Name	Designing and Implementing a Microsoft Azure AI Solution
Exam Code	AI-102
Exam Registration Price	$165
Official Information	https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
See Expected Questions	Microsoft AI-102 Expected Questions in Actual Exam
Take Self-Assessment	Use Microsoft AI-102 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure

Microsoft AI-102 Exam Topics :

Section	Weight	Objectives
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)
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 by using the OCR API extract text from images or PDFs by using the Read API convert handwritten text by using Ink Recognizer extract information from forms or receipts by using the pre-built receipt model in Form Recognizer build andoptimize 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 customobject 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 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 Text Analytics service retrieve and process key phrases retrieveand 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 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 entitiesouse 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 QnA Maker create a QnA 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 add language generation for a response design and implement adaptive cards Create a bot by using the Bot Framework SDK implement dialogs maintain state implement logging for a bot conversation implement a prompt for user input add and review bot telemetry implement a bot-to-human handoff troubleshoot a conversational bot add a custom middleware for processing user messages manage identity and authentication implement channel-specific logic publish a bot 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 by using the Bot Framework Emulator publish a bot Integrate Cognitive Services into a bot integrate a QnA Maker service integrate a LUIS service integrate a Speech service integrate Dispatch for multiple language models manage keys in app settings file

Updates in the Microsoft AI-102 Exam Syllabus:

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