AI-102 Microsoft Azure AI Solution Practice test 2023

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Pass your AI-102: Designing and Implementing a Microsoft Azure AI Solution exam on the first attempt and become Microsoft Certified AI Engineer. Every question has a detailed explanation of why an option is correct and why the other options are wrong. All questions and answers in these practice exams have been carefully design and updated to be fit for Microsoft Azure AI Solution exam. Exam details according to Microsoft, require minimum 70% score to pass: Plan and manage an Azure AI solution (25-30%)Select the appropriate Azure AI service Select the appropriate service for a vision solution Select the appropriate service for a language analysis solution Select the appropriate service for a decision support solution Select the appropriate service for a speech solution Select the appropriate Applied AI services Plan and configure security for Azure AI services Manage account keys Manage authentication for a resource Secure services by using Azure Virtual Networks Plan for a solution that meets Responsible AI principles Create and manage an Azure AI service Create an Azure AI resource Configure diagnostic logging Manage costs for Azure AI services Monitor an Azure AI resource Deploy Azure AI services Determine a default endpoint for a service Create a resource by using the Azure portal Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline Plan a container deployment Implement prebuilt containers in a connected environment Create solutions to detect anomalies and improve content Create a solution that uses Anomaly Detector, part of Cognitive Services Create a solution that uses Azure Content Moderator, part of Cognitive Services Create a solution that uses Personalizer, part of Cognitive Services Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services Implement image and video processing solutions (15-20%)Analyze images Select appropriate visual features to meet image processing requirements Create an image processing request to include appropriate image analysis features Interpret image processing responses Extract text from images Extract text from images or PDFs by using the Computer Vision service Convert handwritten text by using the Computer Vision service Extract information using prebuilt models in Azure Form Recognizer Build and optimize a custom model for Azure Form Recognizer Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services Choose between image classification and object detection models Specify model configuration options, including category, version, and compact Label images Train custom image models, including classifiers and detectors Manage training iterations Evaluate model metrics Publish a trained iteration of a model Export a model to run on a specific target Implement a Custom Vision model as a Docker container Interpret model responses Process videos Process a video by using Azure Video Indexer Extract insights from a video or live stream by using Azure Video Indexer Implement content moderation by using Azure Video Indexer Integrate a custom language model into Azure Video Indexer Implement natural language processing solutions (25-30%)Analyze text Retrieve and process key phrases Retrieve and process entities Retrieve and process sentiment Detect the language used in text Detect personally identifiable information (PII)Process speech Implement and customize text-to-speech Implement and customize speech-to-text Improve text-to-speech by using SSML and Custom Neural Voice Improve speech-to-text by using phrase lists and Custom Speech Implement intent recognition Implement keyword recognition Translate language Translate text and documents by using the Translator service Implement custom translation, including training, improving, and publishing a custom model Translate speech-to-speech by using the Speech service Translate speech-to-text by using the Speech service Translate to multiple languages simultaneously Build and manage a language understanding model Create intents and add utterances Create entities Train evaluate, deploy, and test a language understanding model Optimize a Language Understanding (LUIS) model Integrate multiple language service models by using Orchestrator Import and export language understanding models Create a question answering solution Create a question answering project Add question-and-answer pairs manually Import sources 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 Create a multi-language question answering solution Create a multi-domain question answering solution Use metadata for question-and-answer pairs Implement knowledge mining solutions (5-10%)Implement a Cognitive Search solution Provision a Cognitive Search resource Create data sources Define an index Create and run an indexer Query an index, including syntax, sorting, filtering, and wildcards Manage knowledge store projections, including file, object, and table projections Apply AI enrichment skills to an indexer 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 incremental enrichment Implement conversational AI solutions (15-20%)Design and implement conversation flow Design conversational logic for a bot Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot Build a conversational bot Create a bot from a template Create a bot from scratch Implement activity handlers, dialogs or topics, and triggers Implement channel-specific logic Implement Adaptive Cards Implement multi-language support in a bot Implement multi-step conversations Manage state for a bot Integrate Cognitive Services into a bot, including question answering, language understanding, and Speech service Test, publish, and maintain a conversational bot Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app Test a bot in a channel-specific environment Troubleshoot a conversational bot Deploy bot logic