Practice Exams Microsoft Azure AI-102 Azure AI Solution

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In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them. The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time. Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions. The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item B  last time you went through the test. NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material. Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of 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, integration, maintenance, performance tuning, and monitoring. These professionals work with solution architects to translate their vision and with data scientists, data engineers, Io T specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions. Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available data storage options. Azure AI engineers also need to understand and be able to apply responsible AI principles.    Plan and manage an Azure AI solution (25-30%)    Implement image and video processing solutions (15-20%)    Implement natural language processing solutions (25-30%)    Implement knowledge mining solutions (5-10%)    Implement conversational AI solutions (15-20%)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 image classification and object detection    Manage training iterations    Evaluate model metrics    Publish a trained 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 an orchestration workflow    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 The exam is available in the following languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia)