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Google Professional Cloud Architect Study Guide

Name: Google Professional Cloud Architect Study Guide
Author: Patrick Haggerty
ISBN: 9781098154769

by Patrick Haggerty

October 2026

Intermediate to advanced

590 pages

19h 53m

English

O'Reilly Media, Inc.

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Includes

Quizzes

Preface
Is This Book for You?Who Am I, and Why Should You Listen to Me?How This Book Is OrganizedConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. GCP Core Compute and Data Services
Compute: Powering Your WorkloadsGCE: Familiar and Often the Wrong ChoiceGoogle Kubernetes Engine (GKE): Running Containers Your WayCloud Run: The Stateless Container Easy ButtonGoogle App Engine: A Pioneer in Platform as a Service (PaaS)Cloud Run Functions: Serverless, Event-Driven ComputingAI and ML Compute: GPUs, TPUs, and the AI HypercomputerGoogle Cloud VMware Engine: Bring Your VMware Estate As IsCompute Technologies: That’s a WrapStorage: Where Your Data LivesGoogle Cloud Storage: Offsite File Storage at Its BestBlock Storage in Google Cloud: Persistent Disks, Hyperdisk, and Local SSDsFilestore: A Managed NASCloud SQL: Classic Relational Databases Made EasyAlloyDB: PostgreSQL, but with a Nitro BoostCloud Spanner: A Database That (Almost) Defies PhysicsFirestore: The Flexible, Scalable Database for Modern AppsBigtable: Your Database for When Things Get Seriously FastMemorystore: Because Sometimes, Speed Is EverythingBigQuery: Its Name Sorta Says It AllHow This Fits in the ExamThe Art of Compromise (and Exam Success)Matching Business Requirements to Services: The “GCP Dating Game”Playing GCP MatchmakerNext Steps
2. The Virtual Private Cloud: Connecting Your Services
Networking: Building Your Cloud HighwaysVirtual Private Cloud (VPC): A Global Box of Networking StuffSubnets and IP Addressing: Dividing Your Network LogicallyDNS and NAT: Working with the InternetDesigning Network Connectivity: It’s ImportantInternal Communication: VPC Peering, Shared VPCs, and Network Segmentation StrategiesExternal Connectivity: Getting Traffic InHybrid Connectivity: Linking On-Premises and Other CloudsNetwork Security: Protecting Your Cloud AssetsSecurity Perimeter and Access Controls: Your Cloud’s First Line of DefenseCloud Armor: Protecting Against DDoS and Web AttacksAdvanced Network Security: Fortifying Your Cloud DefensesHow This Fits in the ExamNext Steps
3. Security and IAM—It Starts on Day 0
IAM: The GatekeeperIdentitiesUsers, Groups, and Service AccountsPermissions and RolesBuilding and Using Resource Hierarchies (No, It’s Not Just to Keep Things Organized)Managing Permissions and Not Working Harder Than You ShouldBeyond the IAM BasicsIdentity-Aware Proxy (IAP): VPNless Web App SecurityContext-Aware Access and Chrome Enterprise PremiumSecuring Workload Service AccessOrganization Policies: When Permissions Aren’t EnoughEncryption at Rest and in TransitEncryption at RestEncryption in TransitCompliance and AuditingKey Google Cloud Security CertificationsDiscovering and Protecting Sensitive DataCloud Logging and MonitoringVulnerability Scanning: Stay Ahead of Potential ThreatsSecuring the Software Supply ChainSecuring AISecurity Through the Well-Architected FrameworkHow This Fits in the ExamNext Steps
4. Design Principles: Business Use Cases and Trade-Offs
The Well-Architected Framework: The Spine Behind Every Good DecisionUnderstanding Business RequirementsGathering RequirementsMeasuring Success: KPIs and Metrics to Track the Solution’s EffectivenessThe Importance of FlexibilityBalancing Agility and Stability: Building for Change Without Sacrificing ReliabilityModular Design and Microservices: Breaking Down the MonolithPlanning for the Future: Designing for AdaptabilityCost Optimization: Getting Your Money’s WorthThe CapEx-to-OpEx Shift: Why Cloud Changes the Financial ConversationThe Right Tool for the Job: Efficient Service Selection for a Workload’s NeedsPay-as-You-Go Versus Committed Use: Balancing Flexibility and PredictabilityResource Optimization: Autoscaling, Rightsizing Instances, and Eliminating WasteDesigning for Availability and ResilienceSLIs, SLOs, and SLAs: Defining What “Up” Means for Your BusinessRedundancy and Failover: Using Regions, Zones, and Load Balancing EffectivelyDisaster Recovery Planning: Protecting Against the Worst-Case ScenarioDesigning for Sustainability: The Pillar Everyone ForgetsTrade-Offs: The Architect’s Constant CompanionCost Versus Performance: Striking the Right Balance for the WorkloadSecurity Versus Convenience: Protecting Data While Ensuring UsabilityComplexity Versus Speed of Execution: When Simpler Is the Faster WayHow This Fits in the ExamAnalyzing Questions and Identifying Key Business RequirementsEvaluating Design DecisionsJustifying Trade-OffsNext Steps
5. Compute and Data Service Selection: The Right Service in the Right Place
Making Smart Compute ChoicesThe Art of Rightsizing: From VMs to ServerlessWhen Containers Make Sense (And When They Don’t)Cost-Performance Trade-Offs in Compute SelectionReal-World Compute Selection ScenariosSpecialized Compute for AI and MLStorage Solutions: Picking Your Digital ShelvesMatching Storage Types to Your NeedsBalancing Performance, Cost, and Regional ConsiderationsChoosing a Data-Processing ServiceHow This Fits in the ExamCommon Exam Scenarios and Solution ApproachesService Selection Decision TreesNext Steps
6. Design for Cost Optimization: Saving Money While Getting Things Done
Understanding GCP Pricing ModelsPricing Information and Getting EstimatesPer-Second Billing: Moving from Fine China to Paper PlatesOptimizing Compute CostsCompute Engine: Rightsizing, Spot Instances, CUDs, and Other Ways to Make Pricing Work for YouGKE Standard and Autopilot: Pricing and Pros and ConsCloud Run, Cloud Run Functions, and App Engine: If It’s Running, Then You Are PayingOptimizing Cloud Storage CostsLocation Types: Regional, Dual-Region, and Multi-RegionStorage Classes: Matching Cost to Access FrequencyAutoclass: Hands-Free Storage OptimizationObject Lifecycle Management: Automating Cost Reduction for Aging DataData Compression and Deduplication: Reducing the Storage FootprintPractical Implementation StrategiesDatabases: Cool Features Come at a CostRelational Databases: From Cloud SQL to Spanner, Know What You’re Paying ForNoSQL Choices: How the Spend Really WorksBigQuery: Core CostsMaking Intelligent Trade-OffsNetwork Cost OptimizationUnderstanding Egress and the Money You Spend for ItChoosing the Right Regions and Zones: Communication Costs TooCDNs and Caching: Spend Money to Save MoneyOptimizing Data Transfer Paths: Minimizing Cross-Zone and Cross-Region TrafficPractical Implementation StrategiesMonitoring SpendCloud Billing Reports: Detailed Breakdowns of Your SpendingSetting Budgets and Alerts: Proactive Cost ControlIdentifying Cost Anomalies: Spotting Unexpected Changes or Spikes EarlyRecommender and Active Assist: Let Google Find the SavingsQuotas: Your Secret WeaponPractical Implementation StrategiesCost Optimization and the Well-Architected FrameworkHow This Fits in the ExamTypes of Cost-Related Questions You’ll EncounterKey Knowledge AreasExam Strategy for Cost QuestionsNext Steps
7. Monitoring, Logging, and Observability: SRE the Google Way
The SRE Approach to System ReliabilitySLIs, SLOs, SLAs, and Error BudgetsBuilding a Culture of ObservabilityMonitoring and Alerting SystemsCreating Effective Dashboards and AlertsUptime Checks, Synthetic Checks, and Health MonitoringBuilding Custom MetricsError Reporting and Trace AnalysisComprehensive Logging StrategyAnalyzing Logs with Logs Explorer and AnalyticsCreating Log-Based MetricsLog Routing and Export OptionsUnderstanding Resource Scopes for Observability DataThe Unified Scoping ModelImplementing Multiproject ScopesStrategic Recommendations for Scope DesignIncident Management and RecoveryDesigning Incident Response WorkflowsDisaster Recovery StrategiesConducting Effective PostmortemsEngineering for Reliability: Testing in ProductionOperational Excellence, Reliability, and the Well-Architected FrameworkHow This Fits in the ExamDesigning for Observability from the StartChoosing Appropriate Monitoring and Logging ToolsAnalyzing Scenarios to Identify Potential Failures and Recovery PlansNext Steps
8. Migration and Deployment Strategies: The Journey and Destination
Why Migration Is More Than Moving BitsWhen the Job Itself Changes: How Migration Reshapes Roles and TeamsCommon Migration Pitfalls: What Makes Migrations Fail and How to Avoid ThemAssess and Plan Your MoveUnderstanding Your Workloads: Know Before You GoMigration Center: Your Discovery and Assessment HubWorkload Prioritization: Because You Can’t Move It All at OnceEstimating Migration Effort: The Complexity CalculationMigration Drivers: Know the Motivation for the MoveTechnical Debt: The Cost of Moving As IsChoosing a Migration Strategy: Rehost, Replatform, Refactor, Replace, Retain, and RetireTools for MigrationMigrate to Virtual Machines: Moving VMs to GCE with Minimal ChangesMigrate to Containers: Autocontainerization, the Good and BadDon’t Forget the Data: Database Migration OptionsBigQuery Data Transfer Service: Moving to Your New Data WarehouseTransfer Jobs and Transfer Appliances: Getting Data into Cloud StorageDeployment StrategiesCanary Releasing: It Might Not Be a Coal Mine, But Same IdeaBlue-Green Deployments: Your Safety Net After SuccessRolling Updates and Recreate: The Built-In StrategiesCI/CD Pipelines with Cloud Build: Automating Your Moving CrewInfrastructure as Code and Terraform: The Implementation of DRYHybrid Deployment OptionsSecurely Integrating with Existing On-Premises SystemsMulticloud Deployments: Leveraging the Best of Multiple WorldsManaging Distributed Workloads: Considerations for Latency and GovernanceGoogle Distributed Cloud: GKE All Dressed Up for Different EnvironmentsPost-Migration OptimizationRightsizing Resources: Don’t Waste MoneyLeveraging GCP-Native Services: Moving Cloud NativeMonitoring and Iterative Improvement: Migration Is a Journey, Not a DestinationDisaster Recovery Evolution: Revisiting DR Strategies in Your New Cloud EnvironmentManaging the Human Side of MigrationCultural Change Management: Bringing People Along on the JourneyOperational Readiness: Preparing Teams for New Ways of WorkingTraining and Skill Development: Building Cloud Fluency Across the OrganizationHow This Fits in the ExamCommon Migration Scenarios on the ExamKey Decision Points to RememberWatch for Trade-OffsNext Steps
9. Data Analytics and Big Data on GCP: Piles of Data to Gold
Introduction to Data Analytics on GCP: Something Google’s Good AtThe Value of Data-Driven Decisions: They Don’t Call It BI for NothingGCP’s Data Analytics Ecosystem: Know the ToolsBatch Versus Streaming Analytics: And Why Batch Is EasierData Warehousing and Data LakesBigQuery: The Name Says It AllGemini in BigQuery: Talking to Your DataCloud Storage and Data Lakes: Building Scalable Data RepositoriesData Governance with Knowledge CatalogData Preparation and TransformationDataform and Data Fusion: SQL Transformations and Visual PipelinesDataflow: Managed Beam Runner for Batch and Stream ETLManaged Service for Apache Spark (Managed Spark)Decision Framework for Dataflow Versus Managed Spark Versus BigQueryReal-Time Data IngestionPub/Sub for Streaming: The Backbone of Real-Time AnalyticsStreaming Patterns and ArchitecturesData Visualization and ExplorationData Studio: Creating Dashboards and ReportsLooker: Data-Driven Decisions with Someone Else Writing the SQLConnected Sheets: Analyzing BigQuery Data Directly in SpreadsheetsExploratory Analysis in Notebooks: Agent Platform Workbench, Colab EnterpriseBigQuery ML: When Your Data Warehouse Learns New TricksHow This Fits in the ExamNext Steps

10. Machine Learning and AI on GCP: Teaching Computers to “Think” (Well, Sort Of)
Introduction to AI on GCPWhy Google Is So Good at AI: You Gotta Love Self-InterestHow You Should Approach ML Problems: Pretrained, AutoML, BQML, Model Garden, or CustomLevel 1. Pretrained APIs: The “Just Add Water” OptionLevel 2. Model Garden: The Mall of Pretrained ModelsLevel 3. BigQuery ML: When Your Data’s Already ThereLevel 4. AutoML: Your Data, Google’s ExpertiseLevel 5. Custom Models: When You Need Full ControlPrep Your Data for ML: This Really Is the Hard PartData Quality: Garbage In, Garbage OutFeature Engineering: Data to Leave In, Data to Leave OutData Labeling: Learning from AnswersBuilding and Training ML ModelsAgent Platform Workbench and Colab Enterprise: JupyterLab Notebooks Made EasyGemini Cloud Assist and NotebookLM EnterprisePretrained ML APIs: Vision, Video, Natural Language, Translation, and MoreAutoML: Building Custom Models Without Writing Custom ModelsBuilding AI Agents on the Agent PlatformSecuring AI: Model Armor and Sensitive Data ProtectionDeploying and Serving ML ModelsServing from the Agent Platform: Batch and On-Demand WorkloadsModel Monitoring and Retraining: Maintaining Accuracy Over TimeResponsible AI, Because “We Didn’t Think About That” Isn’t a Good AnswerCI/CD for ML (MLOps): Integrating ML into Your Development PipelinesScaling ML WorkloadsGPUs and TPUs: Choosing Accelerators for Training and InferenceDistributed Training: Scaling Model Training Across Multiple MachinesAgent Platform Pipelines: Kubeflow and TFXHow This Fits in the ExamService Selection ScenariosArchitecture and Design DecisionsCost Optimization ScenariosAI/ML Through the Well-Architected FrameworkPitfalls and TipsNext Steps
11. Serverless Solutions: I Know; the Name Makes My Eye Twitch Too
Introduction to ServerlessFrom VMs to Fully Managed to ServerlessBenefits of Serverless: Scalability, Reduced Operational Overhead, and Reduced TCOWhen to Use Serverless: Event-Driven Workloads, Spiky Traffic, Rapid Prototyping, and Even Steady LoadsGCP Serverless OptionsIt All Started with App Engine: Upload Your Code and GoCloud Run: The New Generation App EngineCloud Run Jobs: When the Work Has a Finish LineWorker Pools and GPUs: Cloud Run Past Request-ServingCloud Run Functions: Event Handling (Though You Might Still Prefer Cloud Run)Eventarc: The Glue That Binds Them All TogetherWorkflows and Managed Service for Apache Airflow: Getting Help with OrchestrationCloud Tasks and Cloud Scheduler: Async Work and Cron JobsArchitecting Serverless ApplicationsMicroservices and Serverless: A Match Made in HeavenEvent-Driven Design: Understanding Triggers, Events, and Data FlowsCold Starts, Execution Environments, and Performance OptimizationIntegration with Other GCP ServicesPub/Sub: Decoupling Serverless Components with MessagingCloud Storage, Firestore, BigQuery: Serverless Data AccessServerless Networking: Reaching Private ResourcesAPI Gateway and Apigee: Providing a Unified InterfaceCloud Tasks: Deferred Work and Reliable ExecutionThe Integration EcosystemMonitoring and ObservabilityCloud Monitoring and Logging: Essential for Serverless WorkloadsError Reporting: Automatic IntegrationDistributed Tracing: Understanding Performance Across ComponentsDebugging Serverless Functions: New ConsiderationsCustom Metrics with OpenTelemetry and PrometheusCost Monitoring for ServerlessHow This Fits in the ExamChoosing the Right Serverless Service for Specific Use CasesDesigning Scalable and Reliable Serverless ArchitecturesUnderstanding the Trade-Offs of Serverless Versus Traditional ArchitecturesNext Steps
12. Development and Operational Processes: Building Systems That Work in the Real World
The Architecture Doesn’t End at DesignWhy Process Matters: Brilliant Designs Fail with Poor ExecutionThe Architect’s Role in the Development Lifecycle: From Design to Deployment and BeyondGoogle’s Approach: SRE Principles Meet Development WorkflowsSoftware Development Lifecycle (SDLC) in the CloudTraditional SDLC Versus Cloud Native SDLC: What Changes When You Move to the CloudIntegrating Architecture into Development: Making Architecture Decisions StickVersion Control and Configuration Management: Infrastructure as Code from Day OneDevelopment Environments: Matching Production Without Breaking the BankCI/CD: Automating the Path to ProductionContinuous Integration Fundamentals: Building and Testing with Every CommitCloud Build and Deployment Pipelines: Automating Builds, Tests, and DeploymentsDeployment Strategies Revisited: Blue-Green, Canary, and Rolling Updates in PracticeInfrastructure as Code: Terraform and Why You Need It AutomatedSecurity in the Pipeline: Scanning, Secrets Management, and Policy EnforcementTesting and ValidationTesting Pyramid in the Cloud: Unit, Integration, System, and Acceptance TestsLoad and Performance Testing: Simulating Real-World Conditions Before Go-LiveChaos Engineering: Breaking Things on Purpose to Build ResilienceValidation Frameworks: Ensuring Infrastructure Meets RequirementsTesting Disaster Recovery: Because Untested DR Plans Aren’t Plans, They’re PrayersService Catalogs and ProvisioningSelf-Service Infrastructure: Empowering Teams While Maintaining ControlService Catalog Patterns: Templates, Guardrails, and Approved ConfigurationsProvisioning Automation: From Request to Running ResourceGovernance Through Process: How Catalogs Enforce StandardsTroubleshooting, Incident Response, and Root-Cause AnalysisObservability-Driven Debugging: Using Logs, Metrics, and Traces to Find ProblemsStructured Troubleshooting: Methodical Approaches to Complex IssuesRoot-Cause Analysis Best Practices: Getting to the Real Problem, Not Just SymptomsPostmortems That Actually Help: Blameless Culture and Learning from FailuresBuilding Runbooks: Documentation That Works When Things Go WrongIncident Response: An Organized, Well-Drilled ProcessHow This Fits in the ExamOperational Excellence, Reliability, and the Well-Architected FrameworkRecognizing Process QuestionsUnderstanding Process Trade-OffsKey Exam Topics from This ChapterApproaching Process Questions on the ExamCommon Traps to AvoidConnecting to Other ChaptersPractice MindsetNext Steps
13. The Human Side: Working with Stakeholders, Driving Change, and Making Decisions That Stick
Stakeholder Management: Influencing and FacilitationIdentifying Stakeholders and Understanding Power DynamicsUnderstanding Needs and Motivations: What Do They Actually Want?Building Trust Through TransparencyCustomer Success ManagementBusiness Continuity as a Stakeholder ConcernNegotiation and Conflict Resolution: You Don’t Get to Always Be RightFacilitation Techniques: Running Productive Architecture DiscussionsDriving Change: Getting People to Actually Adopt Your ArchitectureWhy Technical Solutions Fail Without Change ManagementUnderstanding Resistance: It’s Not Stupidity or StubbornnessBuilding the Business Case: Speaking the Language of ValuePhased Adoption: How to Eat an ElephantTraining and Enablement: Making Success PossibleTeam Assessment and Skills ReadinessEvaluating Current Capabilities: What Can Your Team Actually Do TodayIdentifying Skills Gaps: What Needs to Be Learned Versus What Needs to Be HiredBuilding Versus Buying Versus Partnering: Honest Assessment of Organizational CapacityCreating Learning Pathways: How to Grow the Skills You NeedRightsizing Architecture to Team Capability: Building What Can Be OperatedDecision-Making Processes and FrameworksArchitectural Decision Records: Documenting What You Decided and WhyTrade-Off Analysis: Making Choices Visible and DefensibleRisk Assessment Frameworks: Quantifying Uncertainty for StakeholdersConsensus Versus Consultation Versus Command: When to Use Which Decision-Making ModelAvoiding Analysis Paralysis: Good Enough Decisions Quickly Made Beat Perfect Decisions Made SlowlyCommunicating Architecture and Measuring SuccessTailoring Communication to the AudienceTranslating Technical Decisions into Business LanguageVisual Communication: Diagrams That Tell StoriesDefining Success: What “Working” Actually MeansManaging Expectations and Measuring OutcomesCourse Correction: When Reality Doesn’t Match the PlanBringing It All TogetherHow This Fits in the ExamRecognizing Human Factors in Exam ScenariosCommon Exam Patterns Testing Human SkillsApproaching “Soft Skills” QuestionsConnecting Technical and Human SkillsWhat the Exam Doesn’t TestPractice MindsetWhere That Leaves You
Index
About the Author

Content preview from Google Professional Cloud Architect Study Guide

About the Author

Patrick Haggerty was never quite sure what he wanted to be when he grew up, so he decided he’d just try things until he figured it out. Thrown out of college at 20, he spent four years in the USMC learning responsibility (and to be a better apex predator). Out on a disability, he turned wrenches in a mechanic shop, worked tech support, studied actuarial science, and coded in more languages than he wants to remember. When a job asked him to run some internal training, he discovered a lifelong passion: helping people learn.

Patrick has worked as a professional trainer for more than 30 years and spends most of his days working for ROI Training and Google, helping people learn to leverage Google Cloud.

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Google Cloud Certified Professional Cloud Architect Study Guide, 2nd Edition

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ISBN: 9781098154752Errata Page

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

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Security

Design

Business

Soft Skills

Google Professional Cloud Architect Study Guide

by Patrick Haggerty

About the Author

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,
and much more.