Overview

AI-102: Microsoft Azure AI Engineer Associate Practice Exam is a comprehensive tool designed to help individuals prepare for the Microsoft Azure AI Engineer Associate certification exam. This practice exam covers a wide range of topics related to artificial intelligence, machine learning, and data science, all of which are essential for professionals looking to demonstrate their expertise in designing and implementing AI solutions on the Microsoft Azure platform. With a focus on real-world scenarios and hands-on experience, this practice exam provides a realistic simulation of the actual certification exam, allowing candidates to assess their knowledge and skills in a test environment.AI-102 practice exam includes a variety of questions that test the candidate's understanding of key concepts such as natural language processing, computer vision, and conversational AI. By practicing with these questions, individuals can identify areas where they may need to further study and improve their knowledge. Additionally, the practice exam provides detailed explanations for each question, helping candidates understand the reasoning behind the correct answers and reinforcing their learning. This feedback is invaluable in helping individuals gauge their readiness for the certification exam and make necessary adjustments to their study plan.AI-102: Microsoft Azure AI Engineer Associate certification is designed for individuals who have a strong understanding of AI concepts and are looking to demonstrate their expertise in implementing AI solutions on Microsoft Azure. This certification covers a wide range of topics, including natural language processing, computer vision, and machine learning. By earning this certification, candidates can showcase their ability to design and implement AI solutions that leverage Azure's powerful AI services.AI-102 exam tests candidates on their ability to design and implement AI solutions that meet specific business requirements. This includes understanding how to use Azure Cognitive Services to build AI applications, as well as how to leverage Azure Machine Learning to train and deploy machine learning models. Candidates will also need to demonstrate their knowledge of how to integrate AI solutions with other Azure services, such as Azure Bot Services and Azure IoT Edge.Prepare for the AI-102 exam, candidates should have a solid understanding of AI concepts and be familiar with Azure services. Microsoft offers a variety of resources to help candidates prepare for the exam, including online training courses, practice exams, and hands-on labs. By earning the AI-102 certification, individuals can validate their expertise in implementing AI solutions on Microsoft Azure and enhance their career opportunities in the field of artificial intelligence.AI-102: Microsoft Azure AI Engineer Associate 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 VUEAI-102: Microsoft Azure AI Engineer Associate 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 logicIn addition to the practice questions and detailed explanations, the AI-102 practice exam also offers a timed environment that simulates the pressure and constraints of the actual certification exam. This feature allows candidates to practice managing their time effectively and build their confidence in tackling the exam within the allotted time frame. By using this practice exam as a study tool, individuals can enhance their exam-taking skills, boost their confidence, and increase their chances of passing the Microsoft Azure AI Engineer Associate certification exam on their first attempt.

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