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Covers AI concepts, AI/ML/Deep Learning/GenAI distinctions, foundation models, MLOps concepts, and generative AI topics including security scoping, prompt engineering, RAG, and RLHF. Includes hands-on lab and sandbox study guidance, plus an outline of AWS services such as SageMaker, Bedrock, Amazon Q, and application-focused AI offerings for text, vision, translation, and compliance.",{"@graph":69,"@context":123},[70,84,106],{"@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":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/aws-certified-ai-practitioner-cheat-sheet/317371/",{"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/aws-certified-ai-practitioner-cheat-sheet/317371.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",41,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the purpose of this AWS AI Practitioner cheat sheet?","Question",{"text":113,"@type":114},"It provides educational, exam-focused quick reference material to help candidates prepare for the AWS AI Practitioner—Beta Exam.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the cheat sheet explain how AI processes information?",{"text":118,"@type":114},"It describes a workflow including data collection, algorithm selection, training, testing, and deployment, emphasizing learning patterns from large datasets.",{"name":120,"@type":111,"acceptedAnswer":121},"Which AI application architecture layers are covered?",{"text":122,"@type":114},"It outlines three layers: the data layer, the model layer, and the application layer, describing each layer’s role in building and using AI systems.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},317371,1790371894,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":31,"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},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","AWS Certified AI Practitioner  \nCheat Sheet  \nQuick Bytes for you before the exam!  \nThe information provided in the Cheat Sheet is for educational purposes only; created in our e forts to help aspirants prepare for the AWS AI Practitioner—Beta Exam. Though references have been taken from AWS documentation, it’s not intended to be 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  \n“AWS AI Practitioner” Certification?  \nSelf-assess yourself with  \n800+ Hands-on-Labs and Cloud Sandbox Hands-on Labs `Cloud Sandbox environments  \n| Index |  |\n| --- | --- |\n| Topics Names | Page No |\n| Fundamentals of A I and ML |  |\n| Artificial Intelligence | 4 |\n| Basic AI Terminologies | 7 |\n| Differences between AI, ML, Deep Learning & Gen AI | 9 |\n| Understanding Foundation Model | 10 |\n| AI Models: Types | 12 |\n| Machine Learning | 14 |\n| ML Pipeline: Components with AWS Services | 15 |\n| Fundamentals of ML Operations (MLOps) | 17 |\n| Amazon SageMaker | 19 |\n| Fundamentals of Generative AI |  |\n| Generative AI | 22 |\n| GenAI Security Scoping Matrix | 25 |\n| Amazon SageMaker JumpStart | 27 |\n| Amazon Bedrock | 28 |\n| Amazon Q | 31 |\n| Applications of Foundation Models |  |\n| Prompt Engineering | 32 |\n| Retrieval Augmented Generation (RAG) | 34 |\n| RLHF-Reinforcement Learning from Human Feedback | 36 |\n| Design and implement pipeline automation |  |\n| Responsible AI | 38 |\n| Amazon SageMaker Clarify | 39 |\n| Amazon SageMaker Model Monitor | 40 |\n| AWS Managed AI Services |  |\n| Amazon Polly | 42 |\n| Amazon Comprehend | 42 |\n\n —Back to Index— 2  \n| Amazon Rekognition | 43 |\n| --- | --- |\n| Amazon Lex | 44 |\n| Amazon Transcribe | 45 |\n| Amazon Translate | 46 |\n| Amazon Mechanical Turk (MTurk) | 47 |\n| Amazon Augmented AI [Amazon A2I] | 48 |\n| AWS DeepRacer | 49 |\n| Security, Compliance, and Governance for AI Solutions |  |\n| Amazon Macie | 50 |\n| AWS PrivateLink | 50 |\n\n —Back to Index— 3  \nFundamentals of A I and ML  \nArtificial Intelligence  \nWhat is Artificial Intelligence?  \n● Artificial Intelligence (AI) is a field of computer science focused on developing systems that can exhibit intelligent behavior, such as reasoning, learning, and autonomous action.  \n● AI functions by combining large amounts of data with intelligent algorithms. These algorithms are trained on the data to learn patterns and make informed decisions. This process enables AI to perform tasks that require human-like intelligence, such as understanding language and driving vehicles.  \n● AWS offers a range of pre-built AI services, as well as customizable infrastructure options, designed to streamline AI development and reduce costs.  \nHow does AI process information and make decisions?  \nData Collection: AI systems require vast amounts of data to learn from. This data can be anything from images and text to numerical values.  \nAlgorithm Selection: The appropriate algorithm is chosen based on the specific task the AI is designed to perform. Common algorithms include:  \n● Machine Learning: It involves training algorithms on large datasets to identify patterns and make predictions.  \n● Deep Learning: A subset of machine learning that uses artificial neural networks to learn from complex patterns.  \n● Natural Language Processing (NLP): AI can understand and respond to human language.  \n● Training: The algorithm is trained on the collected data. This involves feeding the data into the algorithm and allowing it to learn from it.  \n● Testing: The trained algorithm is tested on new data to evaluate its performance.  \n● Deployment: Once the algorithm has been tested and refined, it can be deployed in real-world applications.  \nKey components of AI application architecture:  \nArtificial intelligence architecture consists of three core layers. All the layers run on IT  \ninfrastructure that provides the necessary compute and memory re","cbCaillHAGNQm6hS","https://ap.wps.com/l/cbCaillHAGNQm6hS","pdf",1141196,"English","# Fundamentals of AI and ML\n## Artificial Intelligence\n## Basic AI Terminologies\n## Differences between AI, ML, Deep Learning & GenAI\n## Understanding Foundation Model\n## AI Models: Types\n## Machine Learning\n## ML Pipeline: Components with AWS Services\n## Fundamentals of Machine Learning Operations (MLOps)\n## Amazon SageMaker\n# Fundamentals of Generative AI\n## Generative AI\n## GenAI Security Scoping Matrix\n## Amazon SageMaker JumpStart\n## Amazon Bedrock\n## Amazon Q\n## Applications of Foundation Models\n## Prompt Engineering\n## Retrieval Augmented Generation (RAG)\n## RLHF - Reinforcement Learning from Human Feedback\n## Design and implement pipeline automation\n## Responsible AI\n## Amazon SageMaker Clarify\n## Amazon SageMaker Model Monitor\n## AWS Managed AI Services\n## Amazon Polly, Amazon Comprehend\n## Amazon Rekognition, Amazon Lex\n## Amazon Transcribe, Amazon Translate","[{\"question\":\"What is the purpose of this AWS AI Practitioner cheat sheet?\",\"answer\":\"It provides educational, exam-focused quick reference material to help candidates prepare for the AWS AI Practitioner—Beta Exam.\"},{\"question\":\"How does the cheat sheet explain how AI processes information?\",\"answer\":\"It describes a workflow including data collection, algorithm selection, training, testing, and deployment, emphasizing learning patterns from large datasets.\"},{\"question\":\"Which AI application architecture layers are covered?\",\"answer\":\"It outlines three layers: the data layer, the model layer, and the application layer, describing each layer’s role in building and using AI systems.\"}]","AWS Certified AI Practitioner - Cheat Sheet | PDF",1789932396,126]