{"id":81699,"date":"2022-03-25T01:55:28","date_gmt":"2022-03-25T07:25:28","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=81699"},"modified":"2024-04-17T16:46:43","modified_gmt":"2024-04-17T11:16:43","slug":"microsoft-azure-ai-fundamentals-questions","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/","title":{"rendered":"30 Free Questions on Microsoft Azure AI Fundamentals (AI-900)"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">The AI-900: Microsoft Azure AI Fundamentals certification requires you to have an understanding of the concepts of Artificial Intelligence and Machine Learning, their workloads, and implementation on Azure. <\/span><span style=\"font-weight: 400;\">These free AI-900 exam questions will provide you with an insight into some of the concepts and skills measured in the <a href=\"https:\/\/www.whizlabs.com\/microsoft-azure-certification-ai-900\/\" target=\"_blank\" rel=\"noopener\">AI-900 certification<\/a>.<\/span><\/p>\n<p>Let&#8217;s start learning!<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_computer_vision_workloads_on_Azure\" >Domain : Describe features of computer vision workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure\" >Domain : Describe features of Natural Language Processing (NLP) workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_AI_workloads_and_considerations\" >Domain : Describe AI workloads and considerations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-2\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-3\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_computer_vision_workloads_on_Azure-2\" >Domain : Describe features of computer vision workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-2\" >Domain : Describe features of Natural Language Processing (NLP) workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_AI_workloads_and_considerations-2\" >Domain : Describe AI workloads and considerations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-4\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-5\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-3\" >Domain : Describe features of Natural Language Processing (NLP) workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_conversational_AI_workloads_on_Azure\" >Domain : Describe features of conversational AI workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-6\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-7\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-4\" >Domain : Describe features of Natural Language Processing (NLP) workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_conversational_AI_workloads_on_Azure-2\" >Domain : Describe features of conversational AI workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-8\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-9\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-5\" >Domain : Describe features of Natural Language Processing (NLP) workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-6\" >Domain : Describe features of Natural Language Processing (NLP) workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-10\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-11\" >Domain : Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_features_of_computer_vision_workloads_on_Azure-3\" >Domain : Describe features of computer vision workloads on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_AI_workloads_and_considerations-3\" >Domain : Describe AI workloads and considerations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_Artificial_Intelligence_workloads_and_considerations\" >Domain: Describe Artificial Intelligence workloads and considerations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_Artificial_Intelligence_workloads_and_considerations-2\" >Domain: Describe Artificial Intelligence workloads and considerations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_Artificial_Intelligence_workloads_and_considerations-3\" >Domain: Describe Artificial Intelligence workloads and considerations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-12\" >Domain: Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-13\" >Domain: Describe fundamental principles of machine learning on Azure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.whizlabs.com\/blog\/microsoft-azure-ai-fundamentals-questions\/#Summary\" >Summary<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q1 : You are working for a car dealership. Your boss asks you to provide him with forecast information. Will the new car model be successful or not? The new model has a variety of engine improvements, more comfortable seats, and a sunroof. You compiled the list of data about previous successful models with their characteristics and sales numbers.<\/strong><br \/>\n<strong>What should you do in the pre-processing data stage that would help you predict the new model\u2019s success?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Data selection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Training set selection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Data for model evaluation selection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Feature selection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Data classification<\/span><\/p>\n<p><b>Correct Answer: D<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">During pre-processing, you need to work with data to select features that influence the label prediction. In this problem, features are the engine characteristics (power or volume), seat comforts, etc. They could help the ML model to predict the success of the new car model. Maybe the sunroof is not essential for predicting the label, and we need to discard this feature from the final set of features that we will use for model training.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In short, Feature selection helps us to narrow down the features that are important for our label prediction and discard all features that don\u2019t play or play a minimal role in a label prediction. As a result, our trained model and prediction will be more efficient.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect because they are parts of the different data processing events that are irrelevant to the pre-processing (Training set selection or Data for model evaluation selection) or too generic (Data selection or Data Classification).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Feature selection, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/team-data-science-process\/select-features\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/team-data-science-process\/select-features<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_computer_vision_workloads_on_Azure\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of computer vision workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q2 : You created a Custom Vision model. You want your model to detect trained objects on the photos. <\/strong><strong>What information will you get about each object if you are using an object detection model?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Image type<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Bounding box<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Image category<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Class name<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Probability score<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Content name<\/span><\/p>\n<p><b>Correct Answers: B, D and E<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Object detection is the form of ML that helps to recognize objects on the images. Each recognizable object will be put in the bounding box with the class name and probability score.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here is the Microsoft information about the object detection model.<img decoding=\"async\" class=\"aligncenter wp-image-81701 size-full\" title=\"computer vision workloads on Azure\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2.png\" alt=\"computer vision workloads on Azure\" width=\"840\" height=\"628\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2.png 840w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-300x224.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-768x574.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-562x420.png 562w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-80x60.png 80w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-100x75.png 100w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-180x135.png 180w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-238x178.png 238w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-640x478.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai2-681x509.png 681w\" sizes=\"(max-width: 840px) 100vw, 840px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect because they are not part of the object detection model\u2019s return information.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Object detection, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/detect-objects-images-custom-vision\/1-introduction\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/detect-objects-images-custom-vision\/1-introduction<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of Natural Language Processing (NLP) workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q3 : What are the four types of entities that you can create during the authoring of the LUIS Application?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Machine-Learned<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>List<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>FAQ document<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>RegEx<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Chit-chat<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Pattern.any<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>G. <\/strong>Alternative phrasing<\/span><\/p>\n<p><b>Correct Answers: A, B, D and F<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">We need to create intents, entities, and train a model during an authoring phase for a Language Understanding application. There are four types of entities that we can create, such as, Machine-Learned, List, RegEx, and Pattern.any.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect because they are parts of creating a Knowledge base for Q&amp;A Maker and Azure Bot Service.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about LUIS, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-language-model-with-language-understanding\/2-get-started\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-language-model-with-language-understanding\/2-get-started<\/span><\/a><span style=\"font-weight: 400;\"> , <\/span><a href=\"https:\/\/www.luis.ai\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">https:\/\/www.luis.ai\/<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_AI_workloads_and_considerations\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe AI workloads and considerations<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q4 : What are the main features and capabilities of Azure Machine Learning?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Anomaly Detection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Pipelines<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Object Detection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Automated machine learning<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Azure Machine Learning designer<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Text Analytics<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>G. <\/strong>Data and compute management<\/span><\/p>\n<p><b>Correct Answers: B, D, E and G<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Azure Machine Learning is the foundation for Artificial Intelligence service. It includes four features and capabilities.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automated machine learning<\/b><span style=\"font-weight: 400;\"> &#8211; automated creation of ML models based on your data; doesn&#8217;t require any data science experience.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Azure Machine Learning designer<\/b><span style=\"font-weight: 400;\"> &#8211; a graphical interface for no-code creation of the ML solutions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data and Compute managemen<\/b><span style=\"font-weight: 400;\">t &#8211; cloud-based tools for data science professionals,<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pipelines<\/b><span style=\"font-weight: 400;\"> &#8211; visual designer for creating ML tasks workflow<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><strong>Option A is incorrect.<\/strong> Anomaly Detection &#8211; is one of the key elements of Artificial Intelligence, and it is not a feature of Machine Learning.<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>Option C is incorrect.<\/strong> Object Detection is one of Computer Vision&#8217;s common tasks and is not part of Machine Learning.<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>Option F is incorrect.<\/strong> Text Analytics &#8211; is a feature of Natural language processing and is not a part of Machine Learning.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about the features and capabilities of Machine Learning, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/get-started-ai-fundamentals\/2-understand-machine-learn\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/get-started-ai-fundamentals\/2-understand-machine-learn<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-2\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q5 : When you are creating a Clustering Model, what common ML algorithm are you using?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Multiclass Logistic Regression<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>K-means<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Linear Regression<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Two-Class Neural Network<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Decision Forest Regression<\/span><\/p>\n<p><b>Correct Answer: B<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The Clustering is a Machine Learning form that groups items based on some common properties.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The most common Clustering algorithm is K-means Clustering.<\/span><\/p>\n<p><b>Option A is incorrect<\/b><span style=\"font-weight: 400;\"> because the Multiclass Logistic Regression is a Classification algorithm based on a decision forest algorithm.<\/span><br \/>\n<b>Option C is incorrect<\/b><span style=\"font-weight: 400;\"> because the Linear Regression algorithm is a Regression algorithm based on a linear regression model.<\/span><br \/>\n<b>Option D is incorrect<\/b><span style=\"font-weight: 400;\"> because the Two-Class Neural Network is a Classification algorithm based on a neural network algorithm.<\/span><br \/>\n<b>Option E is incorrect <\/b><span style=\"font-weight: 400;\">because the Decision Forest Regression algorithm is a Regression algorithm based on a decision forest algorithm.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about K-means Clustering, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-clustering-model-azure-machine-learning-designer\/create-training-pipeline\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-clustering-model-azure-machine-learning-designer\/create-training-pipeline<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-3\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q6 : What are the four typical steps of data transformation for model training?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Feature selection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Finding and removing data outliers<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Split data<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Impute missing values<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>ML algorithm selection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Normalize numeric features<\/span><\/p>\n<p><b>Correct Answers: A, B, D and F<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation such as Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features.<\/span><\/p>\n<p><b>Option C is incorrect <\/b><span style=\"font-weight: 400;\">because Split data is coming after data transformation.<\/span><br \/>\n<b>Option D is incorrect <\/b><span style=\"font-weight: 400;\">because ML algorithm selection data is coming after data transformation and Split Data steps.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Azure ML algorithms, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-regression-model-azure-machine-learning-designer\/explore-data\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-regression-model-azure-machine-learning-designer\/explore-data<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_computer_vision_workloads_on_Azure-2\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of computer vision workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q7 : Please select three key fields that Form Recognizer service extracts from the common receipts.<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Today&#8217;s date<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Time of transaction<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Taxes paid<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Source of payment<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Merchant information<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Promotion information<\/span><\/p>\n<p><b>Correct Answers: B, C and E<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Form Recognizer service is one of the Azure Computer vision solutions additional to Computer Vision service, Custom Vision Service and Face service.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Form Recognizer service uses pre-build receipt models to extract such information from receipts: date of transaction, time of the transaction, merchant information, taxes paid and receipt total. The service also recognizes all text on the receipt and returns it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Custom vision, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/analyze-receipts-form-recognizer\/2-receipts-azure\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/analyze-receipts-form-recognizer\/2-receipts-azure<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-2\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of Natural Language Processing (NLP) workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q8 : What services are involved in live speech translation?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Speech Recognition<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Speech-to-Text<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Language Detection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Speech Correction<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Text Analysis<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Machine Translation<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>G. <\/strong>Text-to-Speech<\/span><\/p>\n<p><b>Correct Answers: B, D, F and G<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Live speech translation involves the following sequence of the services during the real-time audio stream- Speech-to-Text -&gt; Speech Correction -&gt; Machine Translation -&gt; Text-to-Speech.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The Microsoft documentation has the following diagram for the speech translation process.<img decoding=\"async\" class=\"aligncenter wp-image-81702 size-full\" title=\"Azure Natural Language Processing workloads\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8.png\" alt=\"speech translation process\" width=\"1099\" height=\"351\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8.png 1099w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8-300x96.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8-1024x327.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8-768x245.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8-640x204.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai8-681x217.png 681w\" sizes=\"(max-width: 1099px) 100vw, 1099px\" \/><\/span><\/p>\n<p><b>Options A is incorrect<\/b><span style=\"font-weight: 400;\"> because the Speech Recognition is not involved in this process. You can define from and to translation languages in Speech Translation service settings.<\/span><br \/>\n<b>Option E is incorrect<\/b><span style=\"font-weight: 400;\"> because we are not using Text Analysis services in this case.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about LUIS, please visit the below URLs: <\/span><a href=\"https:\/\/azure.microsoft.com\/en-us\/services\/cognitive-services\/speech-translation\/#features\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/azure.microsoft.com\/en-us\/services\/cognitive-services\/speech-translation\/#features<\/span><\/a>,\u00a0<a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/translate-speech-speech-service\/1-introduction-speech-translation\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/translate-speech-speech-service\/1-introduction-speech-translation<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_AI_workloads_and_considerations-2\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe AI workloads and considerations<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q9 : You created a Personal Virtual Assistant.<\/strong><br \/>\n<strong>Select all responsible AI principles that your solution must follow.<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Responsiveness<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Privacy and security<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Dependability<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Inclusiveness<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Answerability<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Reliability and safety<\/span><\/p>\n<p><b>Correct Answers: B, D and F<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Microsoft recognizes six principles of responsible AI, mentioned below.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about guiding principles for responsible AI, please visit the below URLs: <\/span><a href=\"https:\/\/www.microsoft.com\/en-us\/ai\/responsible-ai?activetab=pivot1:primaryr6\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/www.microsoft.com\/en-us\/ai\/responsible-ai?activetab=pivot1:primaryr6<\/span><\/a>,\u00a0<a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/responsible-ai-principles\/4-guiding-principles\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/responsible-ai-principles\/4-guiding-principles<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-4\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q10 : You create a regression model with low RMSE and review the best model metrics.<\/strong><br \/>\n<strong>Where on the Residual histogram should the most frequently occurring residual values cluster for your model?\u00a0<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>1<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>0.5<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>0<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>-1<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>2<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>-0.5<\/span><\/p>\n<p><b>Correct Answer: C<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The Residual histogram presents the frequency of residual values distribution. <\/span><b>Residual<\/b><span style=\"font-weight: 400;\"> is the difference between predicted and actual values. It represents the amount of error in the model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If we have a good model, we should expect that most of the errors are small. They will cluster around 0 on the Residual histogram.<img decoding=\"async\" class=\"aligncenter wp-image-81703 size-full\" title=\"Microsoft Azure Residual histogram\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10.png\" alt=\"Microsoft Azure Residual histogram\" width=\"1099\" height=\"699\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10.png 1099w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10-300x191.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10-1024x651.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10-768x488.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10-660x420.png 660w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10-640x407.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai10-681x433.png 681w\" sizes=\"(max-width: 1099px) 100vw, 1099px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0All other options are incorrect.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Azure ML Residual histogram, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/use-automated-machine-learning\/use-auto-ml\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/use-automated-machine-learning\/use-auto-ml<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-5\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q11 : You are creating a pipeline in Azure ML Designer. You need to add a module to execute the programming code. What languages can you use for code execution in Azure ML Designer?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>C++<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Java<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Python<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>TypeScript<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>C#<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>R<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>G. <\/strong>JavaScript<\/span><\/p>\n<p><b>Correct Answers: C and F<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Currently, you can use modules for two Languages only- Python and R.<img decoding=\"async\" class=\"aligncenter wp-image-81704 size-full\" title=\"fundamental principles of machine learning on Azure\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11.png\" alt=\"fundamental principles of machine learning on Azure\" width=\"1030\" height=\"1430\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11.png 1030w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11-216x300.png 216w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11-738x1024.png 738w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11-768x1066.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11-303x420.png 303w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11-640x889.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai11-681x945.png 681w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Azure ML Designer, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/how-to-designer-python\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/how-to-designer-python<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-3\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of Natural Language Processing (NLP) workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q12 : You need to create a language model.<\/strong><br \/>\n<strong>What are the essential elements that you need to supply as data for your language model training?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Verbs<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Utterances<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Intents<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Subjects<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Entities<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Knowledge domains\u00a0<\/span><\/p>\n<p><b>Correct Answers: B, C and E<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">For language model training, we need to provide the following key elements- <\/span><b>Entities<\/b><span style=\"font-weight: 400;\">, <\/span><b>Intents,<\/b><span style=\"font-weight: 400;\"> and <\/span><b>Utterance<\/b><span style=\"font-weight: 400;\">. We can achieve this by using the Azure Cognitive service LUIS portal.<\/span><\/p>\n<p><b>Entity<\/b><span style=\"font-weight: 400;\"> is the word or phrase that is the focus of the utterance, as the word &#8220;light&#8221; in the utterance &#8220;Turn the lights on.\u201d<\/span><\/p>\n<p><b>Intent<\/b><span style=\"font-weight: 400;\"> is the action or task that the user wants to execute. It reflects in utterance as a goal or purpose. We can define intent as &#8220;TurnOn&#8221; in the utterance &#8220;Turn the lights on.\u201d<\/span><\/p>\n<p><b>Utterance<\/b><span style=\"font-weight: 400;\"> is the user&#8217;s input that your model needs to interpret, like &#8220;Turn the lights on&#8221; or &#8220;Turn on the lights&#8221;.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Language modelling, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-language-model-with-language-understanding\/1-introduction\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-language-model-with-language-understanding\/1-introduction<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_conversational_AI_workloads_on_Azure\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of conversational AI workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q13 : What service does provide a user interface for a Conversation AI agent?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Azure Speech<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Bot Framework<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>QnA Maker<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Azure Bot Service<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Computer Vision Service<\/span><\/p>\n<p><b>Correct Answer: D<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><b>\u00a0<\/b><span style=\"font-weight: 400;\">Azure Bot Service provides a user interface and connections to the different channels for Conversation AI agents or bots.<\/span><\/p>\n<p><b>Options A is incorrect<\/b><span style=\"font-weight: 400;\"> because the Azure Speech helps recognize and synthesize speech, recognize and identify speakers, translate live or recorded speech. It doesn&#8217;t provide a user interface for bots.<\/span><br \/>\n<b>Option B is incorrect<\/b><span style=\"font-weight: 400;\"> because the Bot Framework provides additional bots&#8217; capabilities, but it relies on Azure Bot Service to provide a user interface for bots.<\/span><br \/>\n<b>Option C is incorrect<\/b><span style=\"font-weight: 400;\"> because the QnA Maker service provides knowledge base capabilities for bots, but it relies on Azure Bot Service to provide a user interface for bots.<\/span><br \/>\n<b>Options E is incorrect<\/b><span style=\"font-weight: 400;\"> because the Computer Vision service works with images, and It doesn&#8217;t provide a user interface for bots.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Conversation AI agents, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/build-faq-chatbot-qna-maker-azure-bot-service\/1-introduction\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/build-faq-chatbot-qna-maker-azure-bot-service\/1-introduction<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-6\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q14 : Select all models that are part of Supervised ML?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Regression model<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Association<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Classification Model<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Clustering Model<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Anomaly Detection<\/span><\/p>\n<p><b>Correct Answers: A and C<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">A Regression and Classification modeling types are two parts of Supervised machine learning. Both techniques train the models using labeled data- previously acquired or historical data for the label.<\/span><\/p>\n<p><b>Option B is incorrect<\/b><span style=\"font-weight: 400;\"> because Association belongs to Unsupervised machine learning. It establishes associations\/relationships between data objects in large databases and uses data that is not labeled.<\/span><br \/>\n<b>Option D is incorrect<\/b><span style=\"font-weight: 400;\">. Clustering belongs to Unsupervised machine learning.<\/span><br \/>\n<b>Option E is incorrect<\/b><span style=\"font-weight: 400;\"> because Anomaly Detection belongs to Unsupervised Learning. It detects the irregularities in the time series data and uses the data that is not labeled.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Supervised ML, please visit the below URLs: <\/span><a href=\"https:\/\/azure.microsoft.com\/en-us\/overview\/machine-learning-algorithms\/#techniques\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/azure.microsoft.com\/en-us\/overview\/machine-learning-algorithms\/#techniques<\/span><\/a>,\u00a0<a href=\"https:\/\/www.guru99.com\/supervised-vs-unsupervised-learning.html\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/www.guru99.com\/supervised-vs-unsupervised-learning.html<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-7\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q15 : What are the three main authoring tools on the Azure ML Studio home screen?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Notebooks<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Datasets<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Designer<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Experiments<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Compute<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Automated ML<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>G. <\/strong>Pipelines<\/span><\/p>\n<p><b>Correct Answers:\u00a0 A, C and F<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Azure ML Studio has three main authoring tools on its home page-<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Notebooks, Automated ML and Designer.<img decoding=\"async\" class=\"aligncenter wp-image-81705 size-full\" title=\"Microsoft Azure Machine Learning Studio\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15.png\" alt=\"Microsoft Azure Machine Learning Studio\" width=\"1050\" height=\"673\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15.png 1050w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-300x192.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-1024x656.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-768x492.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-655x420.png 655w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-341x220.png 341w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-640x410.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai15-681x436.png 681w\" sizes=\"(max-width: 1050px) 100vw, 1050px\" \/><\/span><\/p>\n<p><b>Option B is incorrect<\/b><span style=\"font-weight: 400;\">. Datasets are part of Assets tools and are not part of authoring tools.<\/span><br \/>\n<b>Option D is incorrect<\/b><span style=\"font-weight: 400;\">. Experiments are part of Assets tools and are not part of authoring tools.<\/span><br \/>\n<b>Option E is incorrect<\/b><span style=\"font-weight: 400;\">. Compute is a part of Manage tools and is not the part of authoring tools.<\/span><br \/>\n<b>Option G is incorrect<\/b><span style=\"font-weight: 400;\">. Pipelines are part of Assets tools and are not part of authoring tools.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Azure ML Studio, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/overview-what-is-machine-learning-studio\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/machine-learning\/overview-what-is-machine-learning-studio<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-4\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of Natural Language Processing (NLP) workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q16 : You are using Text Analytics Entity Recognition API to analyze the following sentence- &#8220;After Peter met Sara at Microsoft headquarters in Paris, they visited the Eiffel tower.&#8221;<\/strong><br \/>\n<strong>How many entities with the category &#8220;Location&#8221; should you expect in the API response?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>0<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>1<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>2<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>3<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>4<\/span><\/p>\n<p><b>Correct Answer: D<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Named Entity Recognition (NER) is a Text Analytics service that helps identify entities in the text and group them into different entity categories, like person, organization, location, event, etc.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You should expect three recognized named entities with the category &#8220;Location&#8221; in the API response- &#8220;headquarters,&#8221; &#8220;Paris,&#8221; and &#8220;Eiffel tower.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Entity Recognition services, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/analyze-text-with-text-analytics-service\/2-get-started-azure\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/analyze-text-with-text-analytics-service\/2-get-started-azure<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_conversational_AI_workloads_on_Azure-2\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of conversational AI workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q17 : The customer service of your company spends a lot of time answering the same questions. They asked you to help them to automate this process. They provided you a Microsoft Excel (*.xlsx) document with frequently asked questions and typical answers. What service will you use to create a knowledge base from this document?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Azure Bot Service<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Custom vision<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Text Analytics<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>QnA Maker<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>LUIS<\/span><\/p>\n<p><b>Correct Answer: D<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">You need to use the QnA Maker service. First, you need to provision the QnA Maker resource in your Azure subscription. After that, you can populate the newly created knowledge base using frequently asked questions (FAQ) document.<\/span><\/p>\n<p><b>Option A is incorrect<\/b><span style=\"font-weight: 400;\">. Azure Bot Service facilitates access to the knowledge base, but this service doesn&#8217;t create a knowledge base.\u00a0<\/span><br \/>\n<b>Option B is incorrect<\/b><span style=\"font-weight: 400;\">. Custom vision service helps create your computer vision model, but this service doesn&#8217;t create a knowledge base.\u00a0<\/span><br \/>\n<b>Option C is incorrect<\/b><span style=\"font-weight: 400;\">. Text Analytics helps analyze text documents, detect document&#8217;s language, extract key phrases, determine entities, and provide sentiment analysis. But this service doesn&#8217;t create a knowledge base.\u00a0<\/span><br \/>\n<b>Option E is incorrect<\/b><span style=\"font-weight: 400;\">. Language Understanding Intelligent Service (LUIS) helps understand voice or text commands. But this service doesn&#8217;t create a knowledge base.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about QnA Maker, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/qnamaker\/concepts\/plan\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/qnamaker\/concepts\/plan<\/span><\/a>,\u00a0<a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/QnAMaker\/quickstarts\/create-publish-knowledge-base\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/QnAMaker\/quickstarts\/create-publish-knowledge-base<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-8\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q18 : Please select all examples of Classification models.<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Predicting stock price based on earnings report<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Bank&#8217;s assessment of the customer ability to pay back the loan<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Defining marketing groups by similar buying habits<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Monitoring time-series data for supervised anomaly detection<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Predicting the score of the game<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Reading text in images<\/span><\/p>\n<p><b>Correct Answers: B, D and F<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The Classification is a Machine Learning form that predicts the category or class of the label. The classification model belongs to Supervised ML learning. It trains the models using previously acquired or historical data for the label.<\/span><\/p>\n<p><b>Option B is correct<\/b><span style=\"font-weight: 400;\"> because, for the bank&#8217;s assessment, you need to create a Classification model that, based on historical data, defines classes or categories for the customers&#8217; ability to pay back the loan, like &#8220;yes&#8221; or &#8220;no&#8221; categories.<\/span><br \/>\n<b>Option D is correct<\/b><span style=\"font-weight: 400;\"> because, for supervised anomaly detection, you need to create a Classification model that, based on historical data, defines &#8220;normal and &#8220;abnormal&#8221; classes or categories for detecting the data irregularities in time-series data.<\/span><br \/>\n<b>Option F is correct<\/b><span style=\"font-weight: 400;\"> because we need to use Optical Character Recognition (OCR) technique for reading text in images. OCR utilizes a multi-class Classification model.<\/span><br \/>\n<b>Option A is incorrect<\/b><span style=\"font-weight: 400;\"> because you need to use a Regression Machine Learning model for a numeric prediction (stock price) but not a Classification model.<\/span><br \/>\n<b>Option C is incorrect<\/b><span style=\"font-weight: 400;\"> because you need to use a Clustering Machine Learning model for grouping buyers based on their buying habits but not a Classification model.<\/span><br \/>\n<b>Option E is incorrect<\/b><span style=\"font-weight: 400;\"> because you need to use a Regression Machine Learning model for a numeric prediction (game score) but not a Classification model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Classification ML, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-classification-model-azure-machine-learning-designer\/introduction\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-classification-model-azure-machine-learning-designer\/introduction<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-9\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q19 : You are creating a Compute cluster for the production environment. You set the maximum number of nodes to 5. What should be the value for the minimum number of nodes?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>5<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>0<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>2<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>1<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>3<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>4<\/span><\/p>\n<p><b>Correct Answer: B<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The minimum number of nodes in the production environment should be 0. With this setting, a compute cluster will be automatically stopped (deallocated) during an idle and start up when you need. It will save costs and energy.<img decoding=\"async\" class=\"aligncenter wp-image-81706 size-full\" title=\"Azure Machine Learning New compute cluster\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19.png\" alt=\"Azure Machine Learning New compute cluster\" width=\"1099\" height=\"1001\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19.png 1099w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19-300x273.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19-1024x933.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19-768x700.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19-461x420.png 461w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19-640x583.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai19-681x620.png 681w\" sizes=\"(max-width: 1099px) 100vw, 1099px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Azure ML Compute, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-classification-model-azure-machine-learning-designer\/create-compute\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-classification-model-azure-machine-learning-designer\/create-compute<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-5\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of Natural Language Processing (NLP) workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q20 : What markup language will you use for the control of Speech Synthesis output for your phone auto attendant?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>HTML<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>SQL<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>SSML<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>JSON<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>TeX<\/span><\/p>\n<p><b>Correct Answer: C<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Azure Cognitive services use Speech Synthesis Markup Language (SSML) for control of Speech Synthesis output. Using SSML and XML-based language, you can change the voice speed and pitch and regulate how the text or the text\u2019s parts should be read.<\/span><\/p>\n<p><b>Options B is incorrect<\/b><span style=\"font-weight: 400;\"> because SQL (Structured Query Language) is a data management language, not a markup language.<\/span><br \/>\n<b>Options D is incorrect<\/b><span style=\"font-weight: 400;\"> because JSON (JavaScript Object Notation) is a data-interchange format, and it is not a markup language.<\/span><br \/>\n<b>Option A and E are incorrect<\/b><span style=\"font-weight: 400;\">. Even HTML (Hypertext Markup Language) and TeX are markup languages, but the Azure Cognitive services use SSML for the control of Speech Synthesis output.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about SSML, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/speech-service\/speech-synthesis-markup?tabs=csharp\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/speech-service\/speech-synthesis-markup?tabs=csharp<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_Natural_Language_Processing_NLP_workloads_on_Azure-6\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of Natural Language Processing (NLP) workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q21 : You need to create a Web Bot using the Azure portal. You have to choose a bot template. <\/strong><\/p>\n<p><strong>What are two SDK languages you can select for the bot template?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>C++<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Node.js<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Python<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>C#<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Java<\/span><\/p>\n<p><b>Correct Answers: B and D<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Azure Bot Framework SDK provides templates only for two languages: C# and Node.js.<img decoding=\"async\" class=\"aligncenter wp-image-81707 size-full\" title=\"Microsoft Azure bot template\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21.png\" alt=\"Microsoft Azure bot template\" width=\"1040\" height=\"1429\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21.png 1040w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21-218x300.png 218w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21-745x1024.png 745w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21-768x1055.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21-306x420.png 306w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21-640x879.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai21-681x936.png 681w\" sizes=\"(max-width: 1040px) 100vw, 1040px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options are incorrect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Bot Framework Templates, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/bot-service\/abs-quickstart?view=azure-bot-service-4.0\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/bot-service\/abs-quickstart?view=azure-bot-service-4.0<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-10\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q22 : Your company created a new mobile phone. You need to define a price range (0 &#8211; low cost to 3 &#8211; very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset.<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td><strong>Battery power<\/strong><\/td>\n<td><strong>Clock speed<\/strong><\/td>\n<td><strong>Dual sim<\/strong><\/td>\n<td><strong>Color<\/strong><\/td>\n<td><strong>Internal memory<\/strong><\/td>\n<td><strong>Price range<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>842<\/strong><\/td>\n<td><strong>2.2<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>7<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1021<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>53<\/strong><\/td>\n<td><strong>2<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>563<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>41<\/strong><\/td>\n<td><strong>2<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>615<\/strong><\/td>\n<td><strong>2.5<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>10<\/strong><\/td>\n<td><strong>2<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1821<\/strong><\/td>\n<td><strong>1.2<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>44<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1859<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>22<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1821<\/strong><\/td>\n<td><strong>1.7<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>10<\/strong><\/td>\n<td><strong>3<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1954<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>24<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1445<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>53<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>509<\/strong><\/td>\n<td><strong>0.6<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>9<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>What type of model will you train?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Regression model<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Classification model<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Clustering model<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Unsupervised model<\/span><\/p>\n<p><b>Correct Answer: B<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">We are training the Classification model. In our case, we are using the historical data and predicting the price range category that a new phone belongs to.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The \u201cPrice range\u201d column is our target or label, and it has four classes: 0 (low cost), 1(medium cost), 2(high cost), and 3(very high cost). The model output value will be one of these four classes.<\/span><\/p>\n<p><b>Option A is incorrect.<\/b><span style=\"font-weight: 400;\"> Even the Regression model uses historical data for model training. But it predicts the output numeric value, not the class or classes.<\/span><br \/>\n<b>Option C is incorrect.<\/b><span style=\"font-weight: 400;\"> Clustering model cluster unlabeled data into groups based on some common properties.<\/span><br \/>\n<b>Option D is incorrect<\/b><span style=\"font-weight: 400;\">. An unsupervised model uses unlabeled data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Machine Learning model types, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-classification-model-azure-machine-learning-designer\/introduction\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-classification-model-azure-machine-learning-designer\/introduction<\/span><\/a><span style=\"font-weight: 400;\">,\u00a0<\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-regression-model-azure-machine-learning-designer\/\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-regression-model-azure-machine-learning-designer\/<\/span><\/a>,\u00a0<a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-clustering-model-azure-machine-learning-designer\/\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/create-clustering-model-azure-machine-learning-designer\/<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-11\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q23 : Your company created a new mobile phone. You need to define a price range (0 &#8211; low cost to 3 &#8211; very high cost) for the phone. You collected technical and sales data for the phones on the market and ready to train your model. Here is your train dataset.<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td><strong>Battery power<\/strong><\/td>\n<td><strong>Clock speed<\/strong><\/td>\n<td><strong>Dual sim<\/strong><\/td>\n<td><strong>Color<\/strong><\/td>\n<td><strong>Internal memory<\/strong><\/td>\n<td><strong>Price range<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>842<\/strong><\/td>\n<td><strong>2.2<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>7<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1021<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>53<\/strong><\/td>\n<td><strong>2<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>563<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>41<\/strong><\/td>\n<td><strong>2<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>615<\/strong><\/td>\n<td><strong>2.5<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>10<\/strong><\/td>\n<td><strong>2<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1821<\/strong><\/td>\n<td><strong>1.2<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>44<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1859<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>22<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1821<\/strong><\/td>\n<td><strong>1.7<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>10<\/strong><\/td>\n<td><strong>3<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1954<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>24<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>1445<\/strong><\/td>\n<td><strong>0.5<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>53<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>509<\/strong><\/td>\n<td><strong>0.6<\/strong><\/td>\n<td><strong>1<\/strong><\/td>\n<td><strong>black<\/strong><\/td>\n<td><strong>9<\/strong><\/td>\n<td><strong>0<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>What column will you discard from the final dataset during feature selection?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Battery power<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Clock speed<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Dual sim<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Color<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Internal memory<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>F. <\/strong>Price Range<\/span><\/p>\n<p><b>Correct Answer: D<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Data pre-processing involves various techniques, like feature selection, normalization or feature engineering, etc.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">During feature selection, we identify features that would help us with label prediction. And we discard the rest. In our dataset, the Color feature wouldn&#8217;t correlate with the label due to the constant value of &#8220;black.\u201d We can safely remove this feature from the final dataset.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All other options should be included in the training dataset.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Feature selection, please visit the URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/use-automated-machine-learning\/what-is-ml\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/use-automated-machine-learning\/what-is-ml<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_features_of_computer_vision_workloads_on_Azure-3\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe features of computer vision workloads on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q24 : Can Face service see face makeup in the person\u2019s face image?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Yes<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>No<\/span><\/p>\n<p><b>Correct Answer: A<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Azure Cognitive Face service currently includes the following functionality.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Face detection, Face verification, Find similar faces, Group faces on similarities, and Person identification.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Face API service call returns Face Attributes. Face attributes include age, gender, smile, glasses, emotion, makeup, hair, etc.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here is an example of the JSON result for the makeup attribute.<img decoding=\"async\" class=\"aligncenter wp-image-81708 size-full\" title=\"computer vision workloads on Azure\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai24.png\" alt=\"computer vision workloads on Azure\" width=\"588\" height=\"516\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai24.png 588w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai24-300x263.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/ai24-479x420.png 479w\" sizes=\"(max-width: 588px) 100vw, 588px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Azure Face services, please visit the below URL: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/face\/overview\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/cognitive-services\/face\/overview<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_AI_workloads_and_considerations-3\"><\/span><span style=\"font-weight: 400;\">Domain : <\/span><span style=\"font-weight: 400;\">Describe AI workloads and considerations<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Q25 : What is the main foundation for the Personal Digital Assistant?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A. <\/strong>Azure Speech<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>B. <\/strong>Bot Framework<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>C. <\/strong>Computer Vision Service<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>D. <\/strong>Text Analytics<\/span><br \/>\n<span style=\"font-weight: 400;\"><strong>E. <\/strong>Automated Machine Learning<\/span><\/p>\n<p><b>Correct Answer: B<\/b><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">A Personal Digital Assistant is a solution based on the Bot Framework. It includes three main components, such as, Azure Bot Service, Bot Framework and Knowledge base.<\/span><\/p>\n<p><b>Options A is incorrect<\/b><span style=\"font-weight: 400;\"> because Azure Speech helps recognize and synthesize speech, recognize and identify speakers, translate live, or recorded speech. It is not a foundation for Personal Digital Assistant.<\/span><br \/>\n<b>Options C is incorrect<\/b><span style=\"font-weight: 400;\"> because the Computer Vision service works with images. It is not a foundation for Personal Digital Assistant.<\/span><br \/>\n<b>Option D is incorrect<\/b><span style=\"font-weight: 400;\"> because Text Analytics helps analyze text documents, detect document&#8217;s language, extract key phrases, determine entities, and provide sentiment analysis. It is not a foundation for Personal Digital Assistant.<\/span><br \/>\n<b>Options E is incorrect<\/b><span style=\"font-weight: 400;\"> because Automated Machine Learning is a feature of Machine Learning and is not a foundation for Personal Digital Assistant.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For more information about Personal Assistant, please visit the below URLs: <\/span><a href=\"https:\/\/docs.microsoft.com\/en-us\/azure\/bot-service\/bot-builder-virtual-assistant-introduction?view=azure-bot-service-4.0\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/docs.microsoft.com\/en-us\/azure\/bot-service\/bot-builder-virtual-assistant-introduction?view=azure-bot-service-4.0<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_Artificial_Intelligence_workloads_and_considerations\"><\/span><b>Domain:<\/b><span style=\"font-weight: 400;\"> Describe Artificial Intelligence workloads and considerations<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><strong>Q26:<\/strong> <strong>A news aggregation platform wants to enhance its content moderation efforts to ensure that its articles and user comments adhere to community guidelines and standards. Which Azure AI Content Safety product type would be most suitable for automatically detecting and flagging potentially offensive language or inappropriate content within the text of news articles and user comments?<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> Analyze Image API<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> Jailbreak Risk Detection<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> Analyze Text API<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> Translator Text API<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>E.<\/strong> Protected Material Text Detection<\/span><\/p>\n<p><strong>Correct Answer: C<\/strong><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Because the Analyze Text API offers capabilities for automatically analyzing and flagging potentially offensive language or inappropriate content within text. It provides text analytics features such as sentiment analysis, key phrase extraction, and language detection, making it well-suited for content moderation tasks like ensuring that articles and user comments adhere to community guidelines and standards. Sentiment Analysis classifies text as positive, negative, or neutral based on the sentiment conveyed. The key phrase extraction allows the platform to flag content that deviates from community guidelines. In a multilingual context, language detection is essential for processing content in different languages consistently.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option A is INCORRECT<\/strong> because the Analyze Image API focuses on analyzing images, not text-based content like news articles and user comments. It does not provide the capability to analyze and flag potentially offensive language or inappropriate content within text.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option B is INCORRECT<\/strong> because the Jailbreak Risk Detection primarily focuses on identifying security risks associated with text inputs, such as attempts to exploit vulnerabilities in language models. While it may involve analyzing text, its main purpose is to detect security threats rather than identifying and flagging inappropriate content within news articles and user comments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option D is INCORRECT<\/strong> because the Translator Text API is primarily designed for language translation tasks and is not a product type under the Azure AI Content Safety service.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option E is INCORRECT<\/strong> because the Protected Material Text Detection focuses on detecting protected or copyrighted material within text, rather than analyzing text for inappropriate content. It does not directly address the platform\u2019s need to identify and flag potentially offensive language or inappropriate content within news articles and user comments.<\/span><\/p>\n<p><b>Reference:<\/b> <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/content-safety\/overview?wt.mc_id=AZ-MVP-5004069#product-types\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/content-safety\/overview?wt.mc_id=AZ-MVP-5004069#product-types<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_Artificial_Intelligence_workloads_and_considerations-2\"><\/span><b>Domain:<\/b><span style=\"font-weight: 400;\"> Describe Artificial Intelligence workloads and considerations<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><strong>Q27:<\/strong> <strong>Within Azure AI Document Intelligence, which prebuilt model is designed to autonomously analyze and extract intricate details such as agreement terms and involved parties from legal documents, offering advanced capabilities for automated processing of complex legal agreements?<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong>Invoice<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong>Receipt<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong>Identity<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong>Health Insurance Card<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>E.<\/strong>Contract<\/span><\/p>\n<p><strong>Correct Answer: E<\/strong><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Because the Contract pre-built model is specifically designed to autonomously analyze and extract intricate details such as agreement terms and involved parties from legal documents. This pre-built model offers advanced capabilities for automated processing of complex legal agreements, making it suitable for tasks requiring analysis of legal documents. This pre-built model utilizes advanced natural language processing (NLP) techniques to understand the structure and content of legal agreements.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option A is INCORRECT<\/strong> because the Invoice pre-built model is designed to extract customer and vendor details from financial records, such as billing invoices. Therefore, it is not tailored for processing legal documents.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option B is INCORRECT<\/strong> because the Receipt pre-built model is focused on extracting sales transaction details from purchase receipts. Therefore, it is not tailored for processing legal documents.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option C is INCORRECT<\/strong> because the Identity pre-built model is designed to extract identification and verification details from personal documents, such as driver\u2019s licenses or passports. Therefore, it is not tailored for processing legal documents.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option D is INCORRECT<\/strong> because the Health Insurance Card pre-built model is engineered to retrieve health insurance details from medical records. Therefore, it is not tailored for processing legal documents.<\/span><\/p>\n<p><b>Reference:<\/b> <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/document-intelligence\/overview?view=doc-intel-4.0.0&amp;wt.mc_id=AZ-MVP-5004069#prebuilt-models\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/document-intelligence\/overview?view=doc-intel-4.0.0&amp;wt.mc_id=AZ-MVP-5004069#prebuilt-models<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_Artificial_Intelligence_workloads_and_considerations-3\"><\/span><b>Domain:<\/b><span style=\"font-weight: 400;\"> Describe Artificial Intelligence workloads and considerations<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><strong>Q28:<\/strong> <strong>Which amazing generative AI model has been praised for its exceptional ability to translate textual descriptions into vivid images?<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> GPT-4<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> GPT-3.5\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> Embeddings<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> DALL-E<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>E.<\/strong> Whisper<\/span><\/p>\n<p><strong>Correct Answer: D<\/strong><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">It is specifically used for generating images from textual descriptions. It is trained on a large dataset of image-text pairs and can create vivid images based on textual prompts. The model has demonstrated impressive capabilities in understanding and translating textual descriptions into visual representations, making it a significant advancement in the field of generative AI.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option A is INCORRECT<\/strong> because GPT-4 is not specifically known for generating images from textual descriptions. It is a hypothetical advancement beyond GPT-3, focusing on natural language processing tasks.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option B is INCORRECT<\/strong> because GPT-3.5 is not specialized in generating images from textual descriptions. It\u2019s a variant of the GPT-3 model, primarily designed for natural language processing tasks.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option C is INCORRECT<\/strong> because Embedding is a technique used in natural language processing and other machine learning tasks to represent words or entities as vectors. They are not a specific AI model for generating images.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option E is INCORRECT<\/strong> because Whisper is not a recognized generative AI model for generating images from textual descriptions. It is designed for transcribing and translating speech to text.\u00a0<\/span><\/p>\n<p><b>Reference:<\/b> <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/openai\/concepts\/models\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/openai\/concepts\/models<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-12\"><\/span><b>Domain:<\/b><span style=\"font-weight: 400;\"> Describe fundamental principles of machine learning on Azure<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><strong>Q29:<\/strong> <strong>Which factor contributes to the longer training time required for deep learning algorithms compared to machine learning algorithms?<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> The ability to use small amounts of data for predictions<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> The creation of high-level features from data<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> The division of the learning process into smaller steps<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> The involvement of users in identifying and creating features<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>E.<\/strong> The inherent involvement of numerous layers in deep learning algorithms<\/span><\/p>\n<p><strong>Correct Answer: E<\/strong><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The deep learning models are characterized by their deep architectures, which consist of multiple layers of interconnected neurons. These layers allow the model to learn hierarchical representations of data, where each layer extracts increasingly abstract features from the input data. Training these models often requires processing large amounts of data through numerous layers, leading to longer training times compared to simpler machine learning algorithms.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option A is INCORRECT<\/strong> because the deep learning algorithms typically require large amounts of training data. The use of data for predictions is not a contributing factor to the longer training times.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option B is INCORRECT<\/strong> because while this is a characteristic of deep learning, it\u2019s not directly related to longer training times. Deep learning algorithms can create high-level features autonomously, but this doesn\u2019t necessarily lead to longer training times.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option C is INCORRECT<\/strong> because this option describes a characteristic of machine learning, not deep learning. Deep learning algorithms often tackle problems on an end-to-end basis rather than breaking them into smaller steps.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option D is INCORRECT<\/strong> because in deep learning, features are typically learned from the data automatically, without requiring user input. This involvement of users in feature engineering is more common in traditional machine learning approaches.<\/span><\/p>\n<p><b>Reference:<\/b> <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-deep-learning-vs-machine-learning?view=azureml-api-2#techniques-of-deep-learning-vs-machine-learning\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-deep-learning-vs-machine-learning?view=azureml-api-2#techniques-of-deep-learning-vs-machine-learning<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Domain_Describe_fundamental_principles_of_machine_learning_on_Azure-13\"><\/span><strong>Domain:<\/strong> Describe fundamental principles of machine learning on Azure<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><strong>Q30:<\/strong> <strong>Which of the following best describes the key capability of machine translation achieved through deep learning?<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>A.<\/strong> Identifying and classifying distinct entities within a body of text<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>B.<\/strong> Speech or text translation from one language to another<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>C.<\/strong> Locating and categorizing objects within pictures<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>D.<\/strong> Producing descriptive explanations for images<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>E<\/strong>. Examining large volumes of text data to identify recurring patterns<\/span><\/p>\n<p><strong>Correct Answer: B<\/strong><\/p>\n<p><b>Explanation<\/b><\/p>\n<p><span style=\"font-weight: 400;\">T<\/span><span style=\"font-weight: 400;\">his option correctly describes the key capability of machine translation achieved through deep learning. Machine translation focuses on the automated conversion of text or spoken language from one language to another, devoid of any human intervention. Deep learning models can effectively perform speech or text translation from one language to another by learning patterns and semantics.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option A is INCORRECT<\/strong> because this option refers to named-entity recognition (NER), which involves identifying and categorizing entities like names, dates, locations, etc., within the text. It is not directly related to machine translation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option C is INCORRECT<\/strong> because this option describes object detection, which involves locating and categorizing objects within pictures. While deep learning is used for this purpose, it is not related to machine translation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option D is INCORRECT<\/strong> because this option describes image caption generation, where deep learning models generate descriptive captions for images. While it\u2019s a valid application of deep learning, it\u2019s not specifically related to machine translation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Option E is INCORRECT<\/strong> because this option refers to text analytics, where deep learning methods are used to examine large volumes of text data to identify recurring patterns. While this is a valid application, it\u2019s not directly related to machine translation.<\/span><\/p>\n<p><b>Reference:<\/b> <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-deep-learning-vs-machine-learning?view=azureml-api-2#deep-learning-use-cases\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/concept-deep-learning-vs-machine-learning?view=azureml-api-2#deep-learning-use-cases<\/span><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Summary\"><\/span>Summary<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">We are sure that these AI-900 exam questions and answers with a detailed explanation have helped you with the brushing of the actual exam objectives and you feel more confident than before. For more <\/span><span style=\"font-weight: 400;\">questions on the AI-900 certification, you may try to attempt the practice tests on the official whizlabs page. Stay tuned with Whizlabs.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The AI-900: Microsoft Azure AI Fundamentals certification requires you to have an understanding of the concepts of Artificial Intelligence and Machine Learning, their workloads, and implementation on Azure. These free AI-900 exam questions will provide you with an insight into some of the concepts and skills measured in the AI-900 certification. Let&#8217;s start learning! Domain : Describe fundamental principles of machine learning on Azure Q1 : You are working for a car dealership. Your boss asks you to provide him with forecast information. Will the new car model be successful or not? The new model has a variety of engine 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[15],"tags":[3512],"class_list":["post-81699","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-azure","tag-ai-900-exam"],"uagb_featured_image_src":{"full":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",600,315,false],"thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam-150x150.webp",150,150,true],"medium":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam-300x158.webp",300,158,true],"medium_large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",600,315,false],"large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",600,315,false],"1536x1536":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",600,315,false],"2048x2048":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",600,315,false],"profile_24":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",24,13,false],"profile_48":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",48,25,false],"profile_96":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",96,50,false],"profile_150":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",150,79,false],"profile_300":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",300,158,false],"tptn_thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam-250x250.webp",250,250,true],"web-stories-poster-portrait":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",600,315,false],"web-stories-publisher-logo":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",96,50,false],"web-stories-thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2022\/03\/Free-Questions-on-AI-900-Microsoft-Azure-AI-Fundamentals-Certification-Exam.webp",150,79,false]},"uagb_author_info":{"display_name":"Dharmalingam N","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/dharmalingam\/"},"uagb_comment_info":412,"uagb_excerpt":"The AI-900: Microsoft Azure AI Fundamentals certification requires you to have an understanding of the concepts of Artificial Intelligence and Machine Learning, their workloads, and implementation on Azure. These free AI-900 exam questions will provide you with an insight into some of the concepts and skills measured in the AI-900 certification. Let&#8217;s start learning! 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