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Databricks Certified Generative AI Engineer Associate Study Guide

Name: Databricks Certified Generative AI Engineer Associate Study Guide
Author: Rajaniesh Kaushikk
ISBN: 9798341623453

by Rajaniesh Kaushikk

July 2026

Intermediate to advanced

354 pages

13h 44m

English

O'Reilly Media, Inc.

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Preface
Who Should Read This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. Exam Details and Resources
PrerequisitesLearning ObjectivesCertification Overview and BenefitsExam Format and ObjectivesKey Preparation StrategiesReview the Exam BlueprintGain Hands-On ExperienceUse Databricks Documentation EffectivelyPractice with Sample Questions and Mock ExamsPractical Exam TipsSetting Up Your Databricks WorkspaceStep 1: Open the Databricks WebsiteStep 2: Start the Databricks Sign-up ProcessStep 3: Authenticate with Your Microsoft AccountStep 4: Access the Databricks Account Setup FlowStep 5: Open the Azure Portal to Create the Databricks WorkspaceStep 6: Configure Workspace Settings on the Basics TabStep 7: Configure Networking OptionsStep 8: Review Encryption SettingsStep 9: Review Security and Compliance SettingsStep 10: (Optional) Review TagsStep 11: Validate and Create the WorkspaceStep 12: Create a Machine Learning ClusterStep 13: Create a Notebook and Run a Test CommandSummary
2. Designing Generative AI Applications
PrerequisitesLearning ObjectivesCrafting Prompts for Structured OutputReal-World Examples of Structured PromptingStrategies for Structured PromptingUsing Prompt Templates in CodePrompt Injection Considerations When Using TemplatesMatching Model Tasks to Use CasesDesigning Prompts That Elicit Specifically Formatted ResponsesAI Pipeline CompositionSelecting Chain ComponentsSummarizing Claims with LangChainTemplate Registries and ReusePrompt Registries in Practice: An Open Source ExampleTranslating Use Cases into Input/Output DesignInput/Output Design Use CaseTips for Robust I/O DesignDesigning Reusable Prompt TemplatesIdentifying Prompt/Response Pairs That Align with a Given Model TaskMulti-Stage Reasoning ToolsTool Ordering for Sequential LogicPlanning Agent ChainsAgent Planning with LangChainDesigning Multi-Agent Systems with Tool UseScenario: Insurance Claim ProcessingKey Design Principles of Multi-Agent SystemsEmbedding and Model SelectionSelecting Embedding Model Context Lengths and LLM SizesReading Model Cards to Understand Risk and BiasBalancing Cost, Latency, and Quality When Choosing ModelsSummaryPractice QuestionsHands-On Lab: Multi-Agent Workflow with LangChain + OpenAI in DatabricksScenarioObjective
3. Preparing and Chunking Data for RAG Applications
PrerequisitesLearning ObjectivesRAG Pipeline Overview and Where Chunking FitsDocument Analysis and ChunkingChunking Strategies and When to Use ThemFixed-Length ChunkingSentence-Level ChunkingParagraph-Based ChunkingSliding Window ChunkingSemantic ChunkingControlling Overlap and GranularityOverlapping Chunks for Contextual Flow: Fixed and DynamicDynamic Chunking with Semantic ContinuityImpact of Chunking on RetrievalApplying Chunking Strategy Based on Model ConstraintsContent Filtering and ExtractionRemoving Redundancy and NoiseReal-World Example: Software Documentation PortalExtracting Content from PDFs and ImagesConverting to Delta Format for QueryingChoosing the Correct Python Package for Content ExtractionEvaluating Retrieval QualityUsing Metrics to Improve PerformanceValidating Chunking and Filtering StrategiesMonitoring and Feedback LoopsCorrective Techniques to Resolve Retrieval GapsData Extraction ToolsSummaryPractice Multiple-Choice QuestionsHands-On Lab: Chunking and Indexing for RAGScenarioObjective
4. Building GenAI Applications with Python and LangChain
PrerequisitesLearning ObjectivesLangChain FundamentalsPrompt Templates and ChainsPrompt TemplatesChainsWhy Prompt Templates and Chains MatterMemory and Context ManagementShort-Term Context: Managing Recent InteractionsLong-Term Context: Persisting Relevant InformationContext Injection as a Design PatternFull-History Context Injection (Pattern)Windowed Context Injection (Pattern)Fact Store or Knowledge-Triple Memory (Pattern)Why Memory MattersLangChain Agents OverviewHow Agents WorkBenefits of Using AgentsWhen a Single Agent Is Not EnoughModel and Tool IntegrationPlugging in Tools (APIs, Databases, Calculators)How Tools WorkRAG Integration with ChainsHow Retrieval Works in LangChainExample: Using a Vector Store for RetrievalWhy Retrieval MattersTool Use Cases in Different IndustriesHealthcareFinanceRetailQuality and Safety MechanismsCommon Issues in Model ResponsesReal-World ImpactPrompt Augmentation for SafetyTechniques for Prompt AugmentationExample: Safe SummarizationReal-World ApplicationsBenefits of Prompt AugmentationImplementing GuardrailsTechniques for Implementing GuardrailsExample: Guardrails with Output ValidationReal-World ApplicationsComparison: Without Guardrails Versus with GuardrailsQualitatively Assessing LLM Responses (Quality, Safety, and Hallucination Issues)A Practical Evaluation Workflow for LLM OutputsDefine Quality in an Exam-Aligned WayDefine and Detect HallucinationsAssess Safety and Policy Compliance in OutputsBuild a Qualitative Scoring Rubric You Can ReuseA Databricks-Oriented Test Set StrategyWriting Reviewer Notes That Drive Engineering FixesCommon Qualitative Pitfalls and How to Avoid ThemWriting Metaprompts to Minimize Hallucinations or Data LeakageWhat a Metaprompt Is—and Isn’tWhy Metaprompts Matter for Hallucination ControlCommon Metaprompt Objectives in Databricks ApplicationsStructure of an Effective MetapromptPreventing Data Leakage with MetapromptsMetaprompts and Prompt Injection ResistanceTesting and Iterating on MetapromptsCommon Metaprompt MistakesAgent Frameworks and Multi-Agent SystemsMulti-Stage Reasoning AgentsDesigning Agent Prompt TemplatesMulti-Agent System ArchitectureCreating Tools Needed to Extract Data for Retrieval TasksWhy Tools Are Critical for Retrieval TasksTool Categories Commonly Used in RAG SystemsDesigning a Retrieval Tool: Principles and ConstraintsTools and Retrievers: How They Work TogetherAdding Preprocessing to Extraction ToolsTool Design for Safety and GovernanceWhen to Use Tools Versus Prompt InstructionsSummary of Tool Creation for RetrievalSummaryPractice Multiple-Choice QuestionsHands-On Lab: Building a Retrieval-Augmented GenAI AppScenarioObjective
5. Deploying and Integrating RAG Systems on Databricks
PrerequisitesLearning ObjectivesChain Assembly and TestingPyFunc Model StructureLangChain Chain to Deployable ModelLocal Versus Hosted Deployment PathsCoding a Simple Chain Based on RequirementsCoding a PyFunc Model with Pre- and PostprocessingModel Deployment ProcessMLflow Tracking and RegistrationDeploying to Model Serving EndpointsAccess and Version ControlControlling Access to Model Serving EndpointsVector Search and Endpoint OptimizationVector Search Fundamentals in DatabricksTuning for Latency and CostIncorporating Foundation Model APIsIdentifying Resources Needed to Serve Features in RAG ApplicationsAdvanced Model ServingPre- and Postprocessing in PyFunc ModelsEndpoint Access and Resource ControlBatch Inference Workloads with ai_querySummaryPractice Multiple-Choice QuestionsHands-On Lab: End-to-End RAG System Deployment on DatabricksScenarioObjective
6. Managing Models with MLflow and Unity Catalog
PrerequisitesLearning ObjectivesMLflow Basics and TrackingTracking Experiments and ParametersLogging Models and ArtifactsReviewing Runs and MetricsPutting It All Together: A Simple WorkflowLifecycle and Governance MappingEvaluating RAG Model Performance Using MLflowModel Registration and DeploymentRegistering Models with Unity CatalogManaging Versions with Aliases and TagsModel Lifecycle with AliasesModel Lifecycle Real-World Case Study: Fraud Detection ModelAliases at a GlanceGovernance and Access ControlCase Study: Governance in ActionOperational Best PracticesReproducibility and DocumentationManaging Metadata and Model SignaturesArchiving and Cleanup PoliciesCase Study: Fraud Detection with Governance in ActionUsing Databricks Features to Control LLM CostsSummaryPractice Multiple-Choice QuestionsHands-On Lab: End-to-End Model Management with MLflow and Unity CatalogScenarioObjective
7. Responsible GenAI: Guardrails and Governance
PrerequisitesLearning ObjectivesGuardrail TechniquesPrompt Filtering and RedactionInput Validation and ConstraintsRate Limiting and MonitoringUsing Masking Techniques to Meet Performance or Safety ObjectivesSelecting Guardrail Techniques to Protect Against Malicious InputsData Governance and PrivacyData Masking TechniquesMitigating Problematic TextAdhering to Licensing RequirementsLegal and Licensing Requirements for Data SourcesProviding Alternatives for Problematic Text Mitigation (RAG Input)Risk Management FrameworksResponsible AI ChecklistsLegal and Ethical ConsiderationsAudit Trails and TransparencyLegal and Licensing ConsiderationsLicensing Requirements for External DataData Source Compliance StrategiesLegal Risk MitigationSummaryPractice Multiple-Choice QuestionsHands-On Lab: Implementing AI GuardrailsScenarioObjective
8. Monitoring and Evaluating LLMs in Production Environments
PrerequisitesLearning ObjectivesMetrics and Evaluation CriteriaChoosing Key Performance MetricsQuantitative Evaluation with MLflowGround Truth and Human EvaluationEvaluation Dataset Best PracticesCase Study: Evaluating Hallucination Risk in a Summarization EndpointSelecting LLM Size and Architecture Based on Evaluation MetricsUsing Evaluation Metrics to Compare Model SizesArchitecture Selection as a Feedback LoopPractical Guidance for Production DecisionsMonitoring and LoggingInference Tables and LogsUsing Agent Monitoring ToolsAnomaly Detection and AlertsMonitoring Dashboard Best PracticesAlerting StrategiesCase Study: Identifying a Latency Spike in a Customer Support ChatbotUsing Inference Logging to Evaluate RAG PerformanceUsing Agent Monitoring and Inference TablesCost and Performance OptimizationTracking Token Usage and LatencyResource Allocation StrategiesCost-Saving RecommendationsCase Study: Reducing Token Costs in an ECommerce Recommendation AssistantUsing Databricks Features to Control GenAI CostsAdvanced Evaluation TechniquesHuman Versus Automatic EvaluationEvaluation Versus MonitoringCalibrating Evaluation MetricsCase Study: Calibrating Groundedness Metrics for a RAG-Based Search AssistantIdentifying Which Evaluation Judges Require Ground TruthSummaryPractice QuestionsHands-On Lab: Evaluating and Monitoring LLM PerformanceScenarioObjective
9. Scaling AI Solutions with Vector Search and Mosaic AI
PrerequisitesLearning ObjectivesVector Search FundamentalsCosine Similarity, Dot-Product Similarity, and Vector NormalizationIndexing and Similarity SearchCreating and Querying Vector Search IndexQuerying a Vector Search IndexQuerying with Metadata FiltersRetrievers and Embedding ModelsChoosing the Right Embedding Model: Scenario Decision GuideQuick Scenario-to-Model MappingScaling Applications with Mosaic AIOverview of Mosaic AI ArchitectureModel Serving at ScaleCase Study: High-Throughput RetrievalOptimization and InfrastructureChoosing Context Length and Embedding DimensionsBatch Inference WorkloadsPerformance Testing and ProfilingIdentifying Batch Inference Workloads Suitable for ai_query()SummaryPractice QuestionsHands-On Lab: Building a Scalable Vector Search and Retrieval SystemScenarioObjective

10. Databricks Certified Generative AI Engineer Associate Exam Preparation
PrerequisitesLearning ObjectivesReviewing the Exam BlueprintRecap of All DomainsDomain SummariesHigh-Priority TopicsPractice StrategiesExam Simulation and Practice TestsTime-Management TipsFinal Readiness ChecklistTechnical SetupExam-Day ReadinessPractice ExamSummaryHands-On Lab: End-to-End Generative AI Readiness AssessmentScenarioObjective
About the Author

Content preview from Databricks Certified Generative AI Engineer Associate Study Guide

Chapter 9. Scaling AI Solutions with Vector Search and Mosaic AI

In this chapter, you will learn how scalable vector search and the Mosaic AI platform support high-performance generative AI solutions. Many production applications rely on accurate retrieval, rapid inference, and stable throughput. Accurate retrieval means the system can quickly find the most relevant information from a very large collection of documents, much like pulling the exact book or paragraph you need from a giant library within seconds. If the system retrieves information that is close in wording but not actually useful, the quality of the final AI response suffers. The Databricks Certified Generative AI Engineer Associate exam emphasizes these capabilities because modern workflows must process large document sets, respond to users in real time, and adapt to changing data. Vector search and the supporting infrastructure help you reliably deliver these requirements.

Successful retrieval-augmented generation (RAG) workflows ...

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Databricks Certified Data Engineer Associate Study Guide

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Databricks Certified Generative AI Engineer Associate Study Guide

by Rajaniesh Kaushikk

Chapter 9. Scaling AI Solutions with Vector Search and Mosaic AI

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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