Databricks Certified Generative AI Engineer Associate Study Guide (Rajaniesh Kaushikk) (z-library.sk, 1lib.sk, z-lib.sk)

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Databricks Certified Generative AI Engineer Associate Study Guide Generative AI with Databricks Rajaniesh Kaushikk Foreword by Ari Kaplan

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Databricks Certified Generative AI Engineer Associate Study Guide by Rajaniesh Kaushikk Copyright © 2026 Rajaniesh Kaushikk. All rights reserved. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (https://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Andy Kwan Development Editor: Corbin Collins Production Editor: Jonathon Owen Copyeditor: nSight, Inc. Proofreader: Kim Cofer Indexer: BIM Creatives, LLC Cover Designer: Karen Montgomery Cover Illustrator: José Marzan Jr. Interior Designer: David Futato Interior Illustrator: Kate Dullea July 2026: First Edition

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Revision History for the First Edition 2026-06-24: First Release See https://oreilly.com/catalog/errata.csp?isbn=9798341623453 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Databricks Certified Generative AI Engineer Associate Study Guide, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 979-8-341-62345-3 [LSI]

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Foreword Generative AI is reshaping how organizations interact with data, build applications, and deliver intelligence across the enterprise. What began as experimentation with large language models is quickly evolving into a new application paradigm in which models reason over trusted data, automate complex workflows, and support decision making at scale. As this transition accelerates, the challenge for practitioners is no longer simply understanding how foundation models work. The real opportunity lies in learning how to connect those models to enterprise data, evaluate their behavior, govern their usage, and deploy them reliably in production environments. Certifications such as the Databricks Certified Generative AI Associate play an important role in preparing engineers and data professionals for this shift. Rajaniesh Kaushikk is a Databricks MVP and Databricks Champion. His book provides a practical and well-structured guide to those capabilities. It brings together the essential concepts required to understand generative AI on the Databricks Data Intelligence Platform and presents them in a way that connects certification objectives with implementation patterns used in real-life across modern organizations. Readers are introduced not only to the mechanics of LLMs but also to RAG, vector search, evaluation strategies, and model lifecycle practices that enable production-ready solutions. The book emphasizes the relationship between models and governed enterprise data. Great generative AI systems are built by combining models with reliable data pipelines, scalable infrastructure, and strong governance frameworks. Platforms that unify data, analytics, and AI make this possible, and those who understand this integration will help shape the next generation of intelligent applications. Rajaniesh’s vast experience as a practitioner and educator is evident throughout. He approaches genAI as part of a broader architectural shift toward data intelligence platforms that enable organizations to move from experimentation to production with confidence. This perspective makes the

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book valuable not only for certification candidates but also for engineers and architects who want to understand how generative AI solutions are designed and deployed responsibly at scale. Wherever you are on your journey with generative AI on Databricks— whether you are just starting out, or seeking to strengthen your expertise through certification—this book provides a timely and practical foundation for building the next generation of data-driven AI applications. Ari Kaplan Global Head of Evangelism, Databricks

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Preface Generative AI has rapidly moved from experimental research into the core of modern software systems. Organizations across industries are integrating large language models (LLMs) into applications, data platforms, and operational workflows. What began as experimentation with prompt engineering and isolated model interactions has evolved into production systems that combine embeddings, vector search, retrieval pipelines, evaluation frameworks, and scalable infrastructure. As enterprises adopt these technologies, the role of the data and AI engineer is expanding. Building generative AI applications is no longer limited to training or fine-tuning models. Engineers must design end-to-end architectures that integrate data pipelines, model lifecycle management, retrieval-augmented generation (RAG), monitoring systems, and governance frameworks. Modern data platforms must therefore support not only large-scale data processing but also the operationalization of AI systems. The Databricks Lakehouse platform has emerged as a key environment for building these solutions. With capabilities such as Mosaic AI, vector search, MLflow integration, scalable model serving, and governance through Unity Catalog, Databricks provides a unified platform for developing and deploying generative AI applications. The Databricks Certified Generative AI Engineer Associate exam was introduced to help practitioners validate their skills in building these systems. The certification focuses on practical knowledge needed to design, deploy, and manage generative AI workflows within the Databricks ecosystem. Topics such as embeddings, vector search, RAG, model serving, evaluation techniques, and responsible AI practices are all central to the certification objectives.

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While many resources exist across documentation, tutorials, notebooks, and technical blogs, learners often struggle to find a structured path that connects these concepts cohesively. This book was written to provide that structure. The goal of this book is not only to help readers prepare for the certification exam but also to deepen their understanding of how modern generative AI systems work. Throughout the chapters, you will explore how embeddings capture semantic meaning, how vector search enables efficient retrieval of relevant information, how RAG improves AI responses, and how these components come together to build reliable AI applications. Each chapter connects theoretical concepts to practical implementation patterns, showing how generative AI systems are designed and deployed in real-world environments. Who Should Read This Book This book is designed for a wide range of technical professionals who want to build or operationalize generative AI solutions using Databricks. It will be particularly useful for the following roles: Data engineers who want to extend their skills into generative AI pipelines and retrieval-based architectures Machine learning engineers who want to deploy and manage LLM solutions within modern data platforms AI engineers and application developers who are building applications that integrate LLMs, vector search, and retrieval pipelines Data platform architects and technical leaders who are responsible for designing scalable AI-enabled data platforms Readers are expected to have basic familiarity with Python and general data platform concepts. Prior exposure to Databricks, machine learning

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workflows, or cloud-based data platforms will be helpful but is not strictly required. Even for readers who are not planning to take the certification exam, this book provides a practical foundation for understanding how generative AI systems are designed, deployed, and scaled within modern enterprise environments. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width italic Shows text that should be replaced with user-supplied values or by values determined by context. TIP This element signifies a tip or suggestion. NOTE This element signifies a general note.

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WARNING This element indicates a warning or caution. Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/rkaushik2007/Databricks-Certified- Generative-AI-Engineer-Associate-Study-Guide. If you have a technical question or a problem using the code examples, please send email to support@oreilly.com. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but generally do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Databricks Certified Generative AI Engineer Associate Study Guide by Rajaniesh Kaushikk (O’Reilly). Copyright 2026 Rajaniesh Kaushikk, 979- 8-341-62345-3.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. O’Reilly Online Learning

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NOTE For more than 40 years, O’Reilly Media has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 141 Stony Circle, Suite 195 Santa Rosa, CA 95401 800-889-8969 (in the United States or Canada) 707-827-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://oreilly.com/about/contact.html

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We have a web page for this book, where we list errata and any additional information. You can access this page at https://oreil.ly/databricks-certified- genai. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly. Watch us on YouTube: https://youtube.com/oreillymedia. Acknowledgments This book reflects a journey shaped by the people who have supported and inspired me along the way. I am deeply grateful to my parents, V. D. Kaushik and Kamlesh Kaushik, whose values and encouragement laid the foundation for everything I pursue. My heartfelt thanks to my wife, Kallpana Kaushikk, and my daughter, Aarnna Kaushikk, whose patience, support, and inspiration made this work possible in countless ways. And to my furry companion Casper, who stayed beside me through many late nights of writing, thank you for being part of the journey. I would also like to sincerely thank Prashanth Josyula, Rahul Pathak, Nicolas Bievre, and Vyoma Gajjar for their thoughtful technical reviews and valuable feedback, which helped strengthen the quality of this book. My appreciation also goes to Corbin Collins and the O’Reilly team for their guidance and support throughout the publication process.

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Chapter 1. Exam Details and Resources This chapter introduces the Databricks Certified Generative AI Engineer Associate exam and explains how you should prepare for it. The exam emphasizes practical, applied skills rather than abstract theory. For that reason, understanding the exam structure, objectives, and preparation resources is just as important as learning individual technical concepts. By the end of this chapter, you will have a clear mental model of what the exam tests, how it is delivered, and how to organize your study plan efficiently. If you are new to Databricks or to generative AI workflows, this chapter also helps you set realistic expectations. You will see how Databricks positions generative AI within its broader Lakehouse platform and how that positioning influences exam content. I recommend reading this chapter carefully before starting hands-on labs or advanced model topics, because early clarity will save significant time later. This certification is intended for professionals who design, build, and operationalize generative AI solutions using Databricks, including AI engineers, data scientists, and solution architects working in enterprise environments. Prerequisites Before starting this chapter, you should have the following background: A basic understanding of machine learning concepts such as training, inference, and evaluation Familiarity with Python syntax and notebooks

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High-level awareness of large language models (LLMs) and how they are used in applications You do not need prior experience with Databricks certifications, but some exposure to cloud-based data platforms will be helpful. Learning Objectives After completing this chapter, you will be able to: Explain the purpose and professional value of the Databricks Generative AI Engineer Associate certification. Interpret the exam structure, timing, and scoring model to plan your exam strategy. Identify the major knowledge domains tested on the exam and their relative weight. Configure a Databricks workspace with the core services required for generative AI workloads. Apply proven preparation strategies to maximize exam readiness. Certification Overview and Benefits This section explains why the Databricks Generative AI Engineer Associate certification exists and what value it provides to you and your organization. Understanding the motivation behind the certification helps you align preparation with real-world expectations rather than memorizing isolated facts. The exam validates applied skills used to build and operate generative AI solutions on the Databricks Lakehouse platform. Purpose and value of the certification The Databricks Generative AI Engineer Associate certification validates your ability to design, build, and operationalize generative AI solutions using Databricks-native tools. Unlike general-purpose AI certifications, this

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exam focuses on how generative AI integrates with enterprise data workflows, including data governance, model lifecycle management, and scalable deployment. From an employer’s perspective, the certification signals that you can move beyond experimentation and into production-ready systems. The exam emphasizes practical decision making, such as selecting appropriate model interfaces, integrating with Delta Lake, and applying governance controls. These skills help reduce operational risk and increase trust in generative AI systems. For you as a candidate, the certification provides a structured learning path. Even if you already use generative AI tools, the exam blueprint encourages revisiting fundamentals through the lens of Databricks’ best practices, which often differ from ad hoc notebook experimentation. Career growth and industry applications Generative AI skills are now expected across data engineering, machine learning engineering, and analytics roles. This certification helps you position yourself at the intersection of these disciplines. It demonstrates that you understand not only how models generate text but also how they interact with enterprise data systems. Many candidates also use this certification as a stepping stone. It prepares you for more advanced Databricks certifications and for real-world projects involving retrieval-augmented generation, model serving, and AI governance. Earning the Databricks Certified Generative AI Engineer Associate credential validates your expertise in building LLM-powered applications using Databricks’ AI ecosystem. Here’s why this certification is valuable: Industry recognition Demonstrates proficiency in LLM application design, data preparation, and deployment Career growth

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Opens roles in AI engineering, data science, and MLOps engineering Higher earning potential Certified professionals often command higher salaries due to specialized expertise Competitive advantage Sets you apart in a job market increasingly focused on AI- driven solutions Networking opportunities Access Databricks certified professional community and career resources What can the certification help you do? With this certification, you’ll be able to apply LLMs and AI pipelines in multiple industries: Healthcare AI-powered medical chatbots, patient data retrieval, and disease prediction models Finance Fraud detection, algorithmic trading insights, and automated customer support Retail and ecommerce Personalized recommendations, AI-powered search, and virtual assistants Enterprise AI Automating customer inquiries, summarizing corporate documents, and enhancing productivity with AI-driven

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workflows TIP Highlighting this certification on LinkedIn, your résumé, or during job interviews can help showcase your expertise in generative AI engineering. Exam Format and Objectives This section explains the exam’s structure and the organization of its objectives. Understanding the structure of the objectives helps you prioritize study time and avoid overinvesting in low-impact topics. The official exam blueprint is the authoritative reference for this information. Official exam structure The Databricks Certified Generative AI Engineer Associate exam consists of 45 multiple-choice questions and is administered over 90 minutes. The exam is typically administered online using a proctored testing environment. You must complete the exam in one sitting, and no external aids are allowed. The questions test applied understanding rather than rote memorization. You should expect scenario-based questions that describe a business or technical problem and ask you to select the best solution. Time management is important because the exam length leaves limited time for overanalyzing individual questions. NOTE The exam does not provide access to documentation, so you must internalize core concepts and workflows before test day. Table 1-1 summarizes the exam format. It outlines key logistical and structural aspects of the certification exam, such as duration, number of

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questions, language support, and delivery platform. This table serves as a quick reference for candidates preparing to schedule and take the exam. Table 1-1. Exam format Attribute Details Number of questions 45 Multiple-choice/multiple-selection Time limit 90 minutes Registration fee $200 USD Delivery method Online proctored at https://webassessor.com/databricks Allowed test aids None Exam languages English, ⽇本語, Português BR, 한국어 Prerequisites None, but 6+ months of hands-on experience is recommended Validity period 2 years Understanding scored versus unscored questions Some questions on the exam are unscored and used for future exam development. These questions are indistinguishable from scored questions during the exam. You should treat every question as if it counts toward your final score. This design means that guessing which questions are unscored provides no advantage. Instead, focus on consistent reasoning and eliminating incorrect options based on exam-relevant principles.

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Registration fee, validity, and recertification Databricks sets the exam registration fee, which may vary by region. Once you pass, the certification remains valid for two years. This limited validity reflects the rapid pace of change in generative AI tooling and best practices. After two years, you must recertify to maintain an active status. Recertification typically requires passing the current exam version or a designated renewal assessment. Databricks updates exam objectives to reflect platform changes, so recertification also ensures that your skills remain aligned with current capabilities. You should carefully plan your exam timing. If you work actively with Databricks GenAI features, scheduling the exam shortly after focused preparation helps ensure that knowledge remains fresh and applicable. Domain weightings and focus areas The exam blueprint divides content into several domains, each representing a set of related skills. Domains encompass end-to-end generative AI engineering tasks on Databricks, spanning application design, deployment, governance, and monitoring. Each domain carries a specific weight, meaning some areas contribute more questions than others. Understanding these weightings allows you to prioritize preparation time based on exam impact rather than perceived difficulty. Table 1-2 summarizes this.

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Table 1-2. Official exam domains and weightings Exam domain Weighting Design Applications 14% Data Preparation 14% Application Development 30% Assembling and Deploying Applications 22% Governance 8% Evaluation and Monitoring 12% This table shows that more than half of the exam focuses on application development and deployment. As a result, you should expect many scenario-based questions that test how individual components work together in a complete generative AI solution. TIP Create a study plan that allocates time in proportion to domain weightings. For example, spend more time practicing application development and deployment workflows than purely conceptual topics. Breakdown of exam domains Within each domain, the exam evaluates task-oriented skills rather than abstract definitions. Questions typically describe a scenario with constraints related to governance, scalability, cost, or latency and require you to select the most appropriate approach.