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Candidates for the Azure AI Engineer Associate certification build, manage, and deploy AI solutions that leverage Azure Cognitive Services and Azure Applied AI services.Their responsibilities include participating in all phases of AI solutions development-from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.Candidates for this certification should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.Microsoft Azure AI Solution Exam Summary:Exam Name: Microsoft Azure AI SolutionExam Code: AI-102Exam Price: 165 (USD)Number of Questions: Maximum of 40-60 questions,Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,Length of Test: 130 Minutes. The exam is available in English and Japanese languages.Passing Score: 700 / 1000Languages: English at launch. JapaneseSchedule Exam: Pearson VUEMicrosoft AI-102 Exam Syllabus Topics:Plan and manage an Azure AI solution (25-30%)Select the appropriate Azure AI serviceSelect the appropriate service for a vision solutionSelect the appropriate service for a language analysis solutionSelect the appropriate service for a decision support solutionSelect the appropriate service for a speech solutionSelect the appropriate Applied AI servicesPlan and configure security for Azure AI servicesManage account keysManage authentication for a resourceSecure services by using Azure Virtual NetworksPlan for a solution that meets Responsible AI principlesCreate and manage an Azure AI serviceCreate an Azure AI resourceConfigure diagnostic loggingManage costs for Azure AI servicesMonitor an Azure AI resourceDeploy Azure AI servicesDetermine a default endpoint for a serviceCreate a resource by using the Azure portalIntegrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipelinePlan a container deploymentImplement prebuilt containers in a connected environmentCreate solutions to detect anomalies and improve contentCreate a solution that uses Anomaly Detector, part of Cognitive ServicesCreate a solution that uses Azure Content Moderator, part of Cognitive ServicesCreate a solution that uses Personalizer, part of Cognitive ServicesCreate a solution that uses Azure Metrics Advisor, part of Azure Applied AI ServicesCreate a solution that uses Azure Immersive Reader, part of Azure Applied AI ServicesImplement image and video processing solutions (15-20%)Analyze imagesSelect appropriate visual features to meet image processing requirementsCreate an image processing request to include appropriate image analysis featuresInterpret image processing responsesExtract text from imagesExtract text from images or PDFs by using the Computer Vision serviceConvert handwritten text by using the Computer Vision serviceExtract information using prebuilt models in Azure Form RecognizerBuild and optimize a custom model for Azure Form RecognizerImplement image classification and object detection by using the Custom Vision service, part of Azure Cognitive ServicesChoose between image classification and object detection modelsSpecify model configuration options, including category, version, and compactLabel imagesTrain custom image models, including classifiers and detectorsManage training iterationsEvaluate model metricsPublish a trained iteration of a modelExport a model to run on a specific targetImplement a Custom Vision model as a Docker containerInterpret model responsesProcess videosProcess a video by using Azure Video IndexerExtract insights from a video or live stream by using Azure Video IndexerImplement content moderation by using Azure Video IndexerIntegrate a custom language model into Azure Video IndexerImplement natural language processing solutions (25-30%)Analyze textRetrieve and process key phrasesRetrieve and process entitiesRetrieve and process sentimentDetect the language used in textDetect personally identifiable information (PII)Process speechImplement and customize text-to-speechImplement and customize speech-to-textImprove text-to-speech by using SSML and Custom Neural VoiceImprove speech-to-text by using phrase lists and Custom SpeechImplement intent recognitionImplement keyword recognitionTranslate languageTranslate text and documents by using the Translator serviceImplement custom translation, including training, improving, and publishing a custom modelTranslate speech-to-speech by using the Speech serviceTranslate speech-to-text by using the Speech serviceTranslate to multiple languages simultaneouslyBuild and manage a language understanding modelCreate intents and add utterancesCreate entitiesTrain evaluate, deploy, and test a language understanding modelOptimize a Language Understanding (LUIS) modelIntegrate multiple language service models by using OrchestratorImport and export language understanding modelsCreate a question answering solutionCreate a question answering projectAdd question-and-answer pairs manuallyImport sourcesTrain and test a knowledge basePublish a knowledge baseCreate a multi-turn conversationAdd alternate phrasingAdd chit-chat to a knowledge baseExport a knowledge baseCreate a multi-language question answering solutionCreate a multi-domain question answering solutionUse metadata for question-and-answer pairsImplement knowledge mining solutions (5-10%)Implement a Cognitive Search solutionProvision a Cognitive Search resourceCreate data sourcesDefine an indexCreate and run an indexerQuery an index, including syntax, sorting, filtering, and wildcardsManage knowledge store projections, including file, object, and table projectionsApply AI enrichment skills to an indexer pipelineAttach a Cognitive Services account to a skillsetSelect and include built-in skills for documentsImplement custom skills and include them in a skillsetImplement incremental enrichmentImplement conversational AI solutions (15-20%)Design and implement conversation flowDesign conversational logic for a botChoose appropriate activity handlers, dialogs or topics, triggers, and state handling for a botBuild a conversational botCreate a bot from a templateCreate a bot from scratchImplement activity handlers, dialogs or topics, and triggersImplement channel-specific logicImplement Adaptive CardsImplement multi-language support in a botImplement multi-step conversationsManage state for a botIntegrate Cognitive Services into a bot, including question answering, language understanding,and Speech serviceTest, publish, and maintain a conversational botTest a bot using the Bot Framework Emulator or the Power Virtual Agents web appTest a bot in a channel-specific environmentTroubleshoot a conversational botDeploy bot logicThe AI-102 course is intended for AI engineers, data scientists, and machine learning engineers who want to enhance their skills and capabilities in the field of AI. It is an advanced-level course and requires a solid understanding of AI and machine learning concepts and practices. Candidates who successfully complete the AI-102 course will be able to design and implement advanced AI solutions using Azure AI services and tools.It is recommended that students have completed the Microsoft AI-900 course or have equivalent knowledge before taking the AI-102 course. The AI-102 course is an advanced-level course and requires a solid understanding of AI and machine learning concepts and practices.

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