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AVA®  \nAll rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.  \nEvery effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Orange Education Pvt Ltd or its dealers and distributors, will beheld liable for any damages caused or alleged to have been caused directly or indirectly by this book.  \nOrange Education Pvt Ltd has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capital. However, Orange Education Pvt Ltd cannot guarantee the accuracy of this information. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \nFirst Published: July 2025  \nPublished by: Orange Education Pvt Ltd, AVA®  \nAddress: 9, Daryaganj, Delhi, 110002, India  \n275 New North Road Islington Suite 1314 London, N1 7AA, United Kingdom  \nISBN (PBK): 978-93-49888-15-9  \nISBN (E-BOOK): 978-93-49888-32-6  \nScan the QR code to explore our entire catalogue  \n[www.orangeava.com](www.orangeava.com)  \nTable of Contents  \n1. Introduction to the AWSAI Practitioner Certification Exam  \nIntroduction  \nStructure  \nPurpose and Benefits of the Certification  \nImportance of This Certification  \nRelevance Across Industries  \nBecoming Part ofa Global Community  \nExam Structure and Scoring Methodology  \nExam Format  \nScoring Methodology  \nContent Domains and Weightings  \nDomains and Weightage Distribution  \n1. Fundamentals ofAIandML (20%)  \n2. Fundamentals of Generative AI (24%)  \n3. Applications of Foundation Models (28%)  \n4. Guidelines for Responsible AI (14%)  \n5. Security, Compliance, and Governance for AI Solutions (14%)  \nResources and Study Materials for Preparation  \nAWS Recommended Resources  \nKey Skills Validated by the Certification  \nUnderstanding AI and ML Fundamentals  \nFamiliarity with AWSAI/ML Services  \nApplying AI/ML to Solve Business Problems  \nEnsuring Ethical and Responsible AI Practices  \nSecurity and Compliance in AI Solutions  \nConclusion  \nPoints to Remember  \nReferences  \n2. Overview of AI and ML on AWS  \nIntroduction  \nStructure  \nFundamentals of Artificial Intelligence and Machine Learning  \nCore AI Terms and Concepts  \nArtificial Intelligence (AI): The Brain of the Operation  \nMachine Learning (ML): The Apprentice That Learns  \nDeep Learning (DL): The Prodigy  \nNeural Networks: The Building Blocks of DL  \nComputer Vision (CV): Seeing the World Through AI’s Eyes  \nNatural Language Processing (NLP): Understanding Human  \nLanguage  \nGenerative AI: The Creator in the AI Ecosystem  \nTypes of Data used inAIandML  \nLabeled versus Unlabeled Data: The Guides and the Puzzles  \nTypes of Data: The Ingredients forAIandML  \nAI/ML Algorithms and Techniques: Building the Mall’s Brain  \nHow Machines Learn: The Three Learning Paradigms  \nSupervised Learning: Learning with a Map  \nUnsupervised Learning: Discovering Hidden Patterns  \nReinforcement Learning: Learning by Doing  \nKey Concepts in AI/ML – Training, Inference, and Building a Fair  \nModel  \nTraining versus Inference – The AI Employee ’s Journey  \nReal-Time versus Batch Inference – Handling the Rush  \nBias and Fairness – The Ethical Mall  \nModel Fit – The Right Balance  \nMeasuring Success – Performance Metrics  \nApplications of AI/ML: Real-World Use Cases in the Smart Mall  \nComputer Vision: Eyes ofthe Mall  \nNatural Language Processing (NLP): The Mall’s Voice  \nSpeech Recog","cbCaipLy6pnMQo3H","https://ap.wps.com/l/cbCaipLy6pnMQo3H","pdf",3379861,"English","# Introduction to the AWSAI Practitioner Certification Exam\n## Structure\n## Purpose and Benefits of the Certification\n## Exam Structure and Scoring Methodology\n## Content Domains and Weightings\n## Resources and Study Materials for Preparation\n## Key Skills Validated by the Certification\n# Overview of AI and ML on AWS\n## Fundamentals of Artificial Intelligence and Machine Learning\n## AI/ML Algorithms and Techniques\n## Applications of AI/ML\n## Limitations of AI/ML\n## Overview of AI/ML Workflows on AWS\n# Core AWS Services and Tools for AI and ML\n## Overview of SageMaker\n## Data Preparation and Analytics Services","[{\"question\":\"What are the main content domains and their weightings in the AWSAI practitioner certification exam?\",\"answer\":\"The domains include Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), and Security, Compliance, and Governance for AI Solutions (14%).\"},{\"question\":\"How does the exam scoring methodology relate to the exam format?\",\"answer\":\"The document explains both the exam format and the scoring methodology, including how questions map to content domains and how performance is measured through stated performance metrics.\"},{\"question\":\"Which AWS services are emphasized for building end-to-end AI/ML workflows?\",\"answer\":\"It highlights Amazon SageMaker and its components (e.g., Studio, Data Wrangler, Autopilot, Clarify, Model Monitor) and also data and analytics services such as AWS Glue, AWS Lake Formation, Amazon EMR, and AWS Data Exchange.\"}]","AWSAI Practitioner Certification Exam - Exam Preparation and Learning Assessment | PDF",1789933124,76]