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Covers major Azure AI concepts and exam-aligned topics across AI workloads: AI services and intelligent apps, responsible AI principles, machine learning fundamentals and techniques, automated ML and MLOps, and core computer vision capabilities. Also summarizes generative AI features and Azure OpenAI, plus NLP workload scenarios including key phrase extraction, NER, sentiment analysis, translation, and speech services.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/whizlabs-ai-900-certification-cheat-sheet-azure-ai-fundamentals/469801/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/whizlabs-ai-900-certification-cheat-sheet-azure-ai-fundamentals/469801.png","ImageObject",300,407,{"name":92,"@type":93},"Franzy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this AI-900 cheat sheet?","Question",{"text":112,"@type":113},"It is an educational study aid created to help prepare for the AI-900 certification. It references Microsoft documentation but is intended as a learning supplement rather than a replacement.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which AI workload areas are covered for Azure in this material?",{"text":117,"@type":113},"It covers AI workloads including AI services and intelligent apps, machine learning fundamentals, computer vision, generative AI, and natural language processing scenarios.",{"name":119,"@type":110,"acceptedAnswer":120},"Does the cheat sheet include responsible AI topics?",{"text":121,"@type":113},"Yes. It includes “Responsible AI” guiding principles and related content for Azure AI and Azure OpenAI practices.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},469801,1790875335,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},2336478945519,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","AI-900: Microsoft Azure AI Fundamentals  \nCheat Sheet  \nQuick Bytes for you before the exam!  \nThe information provided in Cheat Sheets is for educational purposes only; created in our e forts to help aspirants prepare for the Exam AI-900 certification. Though references have been taken from Microsoft documentation, it’snot intended as a substitute for the oficial docs. The document can be reused, reproduced, and printed in any form; ensure that appropriate sources are credited and required permissions are received.  \nAre you Ready for MS Azure AI Fundamentals?  \n“AI-900” Certification?  \nSelf-assess yourself with  \nWhizlabs FREE TEST  \n800+ Hands-on-Labs and Cloud Sandbox Hands-on Labs `Cloud Sandbox environment  \n| Index |  |  |\n| --- | --- | --- |\n| Domain Name | Topics | Page no |\n| Describe Artificia l Intelligence workloadsand considerations | AI workloads: Common Features |  |\n|  | Azure AI Services – Using AI for Intelligent Apps | 5 |\n|  | What is Azure Content Moderator? -AI Services | 7 |\n|  | What Is Computer Vision? | 8 |\n|  | Natural language processing technology | 10 |\n|  | Knowledge Mining | 12 |\n|  | What is Azure AI Document Intelligence? | 15 |\n|  | Responsible AI: guiding principles |  |\n|  | What is generative AI? | 16 |\n|  | What is Responsible AI -Azure Machine Learning | 17 |\n|  | Machine learning fairness | 18 |\n|  | AI shared responsibility model -Azure | 20 |\n|  | Transparency Note for Azure AI Studio safety evaluations | 22 |\n| Describe fundamental principles of machine learning on Azure | Identify common machine learning techniques |  |\n|  | Azure Machine Learning-ML as a Service | 23 |\n|  | Machine Learning Algorithms | 25 |\n|  | Linear Regression: Component Reference | 27 |\n|  | How to select algorithms for Azure Machine Learning | 29 |\n|  | Create an Azure Machine Learning compute cluster | 30 |\n|  | Core machine learning concepts |  |\n|  | What Is Deep Learning? -Microsoft Azure | 31 |\n|  | Create and explore datasets with labels | 33 |\n|  | Create a labeled dataset using Azure Machine Learning | 34 |\n|  | Data splits and cross-validation in automated machine | 35 |\n\n–Back to Index–  2  \n|  | Describe Azure Machine Learning capabilities |  |\n| --- | --- | --- |\n|  | What is automated ML? AutoML-Azure Machine Learning | 36 |\n|  | Azure's automated machine learning capabilities | 37 |\n|  | Machine Learning Solutions for Data Scientists | 39 |\n|  | MLOps: Machine learning model management | 41 |\n| Describe features of computer vision workloads on Azure | Identify Azure tools and services for computer vision tasks |  |\n|  | What is Azure AI Vision? | 42 |\n|  | Azure AI Vision with OCR and AI | 43 |\n|  | What is the Azure AI Face service? | 45 |\n|  | Identify common types of computer vision solution |  |\n|  | Image classification on Azure | 47 |\n|  | Object detection-Azure AI Vision | 49 |\n|  | OCR-Optical Character Recognition-Azure AI services | 50 |\n|  | Face detection, attributes, and input data | 51 |\n| Describe features of generative AI workloadson Azure | Identify features of generative AI solutions |  |\n|  | Fundamentals of Generative AI -Training | 53 |\n|  | Generative AI in Azure Machine Learning | 56 |\n|  | 10 ways generative AI and Azure OpenAI Service | 58 |\n|  | Responsible AI Solutions | 59 |\n|  | Identify capabilities of Azure OpenAI Service |  |\n|  | Overview of Responsible AI practices for Azure OpenAI | 62 |\n|  | What is Azure OpenAI Service? | 64 |\n|  | Azure openAI for code generate and development task | 66 |\n|  | Quickstart: Generate images with Azure OpenAI Service | 67 |\n\n–Back to Index–  3  \n| Describe features of Natural Language Processing (NLP) workloads on Azure | NLP Workload Scenarios and Common Features |  |\n| --- | --- | --- |\n|  | What is key phrase extraction in Azure AI Language? | 69 |\n|  | What is Named Entity Recognition (NER) in Azure AI Language? | 71 |\n|  | What is sentiment analysis and opinion mining? | 72 |\n|  | What is Azure AI Language-Azure AI services","cbCaivCMhqQYD11l","https://ap.wps.com/l/cbCaivCMhqQYD11l","pdf",3751103,84,"English","# AI-900 Certification Cheat Sheet - Azure AI Fundamentals\n## AI workloads and common features\n## Machine learning fundamentals on Azure\n## Computer vision workloads on Azure\n## Generative AI workloads on Azure\n## Natural language processing (NLP) workloads on Azure","[{\"question\":\"What is the purpose of this AI-900 cheat sheet?\",\"answer\":\"It is an educational study aid created to help prepare for the AI-900 certification. It references Microsoft documentation but is intended as a learning supplement rather than a replacement.\"},{\"question\":\"Which AI workload areas are covered for Azure in this material?\",\"answer\":\"It covers AI workloads including AI services and intelligent apps, machine learning fundamentals, computer vision, generative AI, and natural language processing scenarios.\"},{\"question\":\"Does the cheat sheet include responsible AI topics?\",\"answer\":\"Yes. It includes “Responsible AI” guiding principles and related content for Azure AI and Azure OpenAI practices.\"}]","Whizlabs AI-900 Certification Cheat Sheet - Azure AI Fundamentals | PDF",1790776264,212]