Google Cloud Professional Cloud Architect Exam Prep

What's inside

1. Level 1: Exam Craft: How the PCA Exam WorksFree
   [Orientation – exam craft] Covers the exam facts and how to study for it: the exam guide and the four case studies (Altostrat Media, Cymbal Retail, EHR Healthcare, KnightMotives Automotive) belong to Google, are not reproduced in this course and are free on Google's certification page; the exam takes 2 hours and has 50-60 questions for $200; the six sections and their weights; and how to answer scenario 'best answer' questions. This course is independent of Google, is not endorsed by it and cannot promise a pass.
2. Level 2: Business Use Cases and Functional vs Non-Functional NeedsFree
   [Section 1: Designing and planning a cloud solution architecture – 1.1] Shows how to turn business use cases and product strategy into functional and non-functional requirements, which are the starting point for every design question on the exam.
3. Level 3: KPIs, ROI, Workload Disposition and Trade-offs
   [Section 1: Designing and planning a cloud solution architecture – 1.1] Covers success measurements (KPIs, ROI and metrics), workload disposition strategies (build, buy, modify or deprecate) and how to weigh up design decision trade-offs.
4. Level 4: Cost, Continuity, Security and Observability as Requirements
   [Section 1: Designing and planning a cloud solution architecture – 1.1] Treats cost optimization, the business continuity plan, security and compliance, and observability as business requirements that shape the design from the start rather than being added later.
5. Level 5: Application Design, Integration and Data Movement
   [Section 1: Designing and planning a cloud solution architecture – 1.1, 1.3] Covers supporting the application design, integration patterns with external systems, movement of data, and integration with on-premises and multicloud environments at the level of architectural patterns.
6. Level 6: The Google Cloud Well-Architected Framework
   [Section 1: Designing and planning a cloud solution architecture – 1.2] Introduces the Google Cloud Well-Architected Framework and its pillars (operational excellence, security, reliability, performance optimization, cost optimization and sustainability), which run through the whole exam.
7. Level 7: High Availability, Fail-over, Backup and Recovery
   [Section 1: Designing and planning a cloud solution architecture – 1.2] Covers high availability and fail-over design across zones and regions, together with backup and recovery choices that meet RTO and RPO targets.
8. Level 8: Elasticity, Scale, Latency and Gemini Cloud Assist
   [Section 1: Designing and planning a cloud solution architecture – 1.2] Covers flexibility of cloud resources, scalability to meet growth requirements, performance and latency, and how Gemini Cloud Assist helps design and operate architectures.
9. Level 9: Designing Cloud-Native Networking
   [Section 1: Designing and planning a cloud solution architecture – 1.3] Covers the cloud-native networking design choices: VPC, peering, firewalls, routing, load balancers, container networking, Shared VPC and Private Service Connect.
10. Level 10: Choosing Storage: Object, File and Databases
    [Section 1: Designing and planning a cloud solution architecture – 1.3] Covers choosing the appropriate storage type (object, file or database) and the right Google Cloud product for each access pattern, consistency need and scale.
11. Level 11: Choosing Data Processing Solutions
    [Section 1: Designing and planning a cloud solution architecture – 1.3] Covers choosing data processing solutions for batch and streaming, using managed services such as Pub/Sub, Dataflow, Dataproc and BigQuery.
12. Level 12: Mapping Compute Needs to Platform Products
    [Section 1: Designing and planning a cloud solution architecture – 1.3] Covers mapping compute needs to GKE, Cloud Run, Cloud Run functions and Compute Engine, and choosing compute resources such as spot VMs, custom machine types and specialized workload machines.
13. Level 13: Choosing Google Cloud AI and ML Solutions
    [Section 1: Designing and planning a cloud solution architecture – 1.3] Covers choosing between Google Cloud AI and machine learning solutions (Gemini LLMs and Gemini models, Agent Builder, Model Garden and AI Hypercomputer), now offered through Gemini Enterprise Agent Platform, which replaced Vertex AI in 2026.
14. Level 14: Migration Planning with Migration Center
    [Section 1: Designing and planning a cloud solution architecture – 1.4] Covers writing a migration plan: integrating with existing systems, assessing systems and data with Google Cloud Migration Center, migration methodologies, workload testing, network planning, dependency planning, and the software licence and financial impact.
15. Level 15: Envisioning Future Solution Improvements
    [Section 1: Designing and planning a cloud solution architecture – 1.5] Covers planning for cloud and technology improvements and for changing business needs, and using a cloud-first design approach that lets an architecture evolve without being rebuilt.
16. Level 16: Hybrid Networking: Cloud VPN and Interconnect
    [Section 2: Managing and provisioning a cloud solution infrastructure – 2.1] Covers configuring extensions to on-premises environments with HA VPN, Dedicated and Partner Interconnect, and Cloud Router dynamic routing.
17. Level 17: Multicloud and Google Cloud-to-Google Cloud Links
    [Section 2: Managing and provisioning a cloud solution infrastructure – 2.1] Covers configuring multicloud connectivity and Google Cloud-to-Google Cloud communication with Cross-Cloud Interconnect, Network Connectivity Center, VPC Network Peering and Private Service Connect.
18. Level 18: Network Security: Firewalls, IPS and Access Control
    [Section 2: Managing and provisioning a cloud solution infrastructure – 2.1] Covers configuring network security protection: VPC firewall rules and policies, intrusion prevention with Cloud Next Generation Firewall, Google Cloud Armor at the edge, and network access control.
19. Level 19: VPC Design and Load Balancing for Access Paths
    [Section 2: Managing and provisioning a cloud solution infrastructure – 2.1] Covers setting up VPC access to the cloud, the internet and cloud-adjacent services: Private Google Access, Cloud NAT, Private Service Connect endpoints, and load balancer backends including hybrid network endpoint groups.
20. Level 20: Configuring Storage Systems End to End
    [Section 2: Managing and provisioning a cloud solution infrastructure – 2.2] Covers configuring individual storage systems: data storage allocation, the compute that processes the data, security and access management, settings for data transfer and latency, data retention and lifecycle management, data growth planning, and data protection through backup and recovery.
21. Level 21: Provisioning Compute: Spot vs Standard and Networking
    [Managing and provisioning a cloud solution infrastructure — 2.3] Learners set up compute resources, choose spot or standard VMs based on how much interruption a workload can tolerate, and configure cloud-native networking for Compute Engine, GKE, serverless networking and Google Cloud VMware Engine.
22. Level 22: Orchestration, Patching, Containers and Serverless
    [Managing and provisioning a cloud solution infrastructure — 2.3] Learners compare infrastructure orchestration, resource configuration and patch management with container orchestration on GKE and serverless computing on Cloud Run and Cloud Run functions.
23. Level 23: Agent Platform Pipelines and Data Integration
    [Managing and provisioning a cloud solution infrastructure — 2.4] Learners use Agent Platform Pipelines in Gemini Enterprise Agent Platform (the 2026 successor to Vertex AI) to automate and orchestrate the ML lifecycle, and prepare data sources for Agent Platform data integration.
24. Level 24: AI Hypercomputer: GPUs, TPUs and Consumption Models
    [Managing and provisioning a cloud solution infrastructure — 2.4] Learners plan ML/AI workloads with AI Hypercomputer, Cloud Run functions and Agent Platform: they add GPUs and TPUs to training and serving, optimise for different consumption models, and run large-scale AI model trainings.
25. Level 25: Google AI APIs, Gemini Enterprise and Model Garden
    [Managing and provisioning a cloud solution infrastructure — 2.5] Learners set up prebuilt solutions with Agent Platform: they tell the Google AI APIs apart (Search, Conversation, Vision, Image, Video and Audio), add Gemini Enterprise features (AI Agents and NotebookLM) to workflows, and integrate models from Model Garden.
26. Level 26: IAM Design: Roles, Principals and Service Accounts
    [Designing for security and compliance — 3.1] Learners design Identity and Access Management (IAM): basic, predefined and custom roles, groups as principals, service accounts, IAM conditions and least privilege, all from the architect's point of view.
27. Level 27: Resource Hierarchy: Organizations, Folders, Projects
    [Designing for security and compliance — 3.1] Learners design a resource hierarchy (organizations, folders and projects) and work out how IAM policy inheritance and environment separation follow from it.
28. Level 28: Data Security: Encryption and Secret Management
    [Designing for security and compliance — 3.1] Learners cover data security: default encryption at rest and in transit, the key management options available, and secret management with Secret Manager instead of embedding credentials.
29. Level 29: Managing CMEK with Cloud KMS
    [Designing for security and compliance — 3.1] Learners manage customer-managed encryption keys (CMEK) with Cloud Key Management Service (Cloud KMS), covering key rings, key rotation, protection levels, the location of keys relative to data, and the effect of disabling or destroying a key.
30. Level 30: Separation of Duties and Auditing Controls
    [Designing for security and compliance — 3.1] Learners apply separation of duties, for example by keeping key administrators apart from key users and splitting projects by function, and design auditing controls with Cloud Audit Logs.
31. Level 31: VPC Service Controls, Org Policy and Firewalls
    [Designing for security and compliance — 3.1] Learners layer the remaining security controls: VPC Service Controls perimeters against data exfiltration, context-aware access, organization policy constraints and hierarchical firewall policies.
32. Level 32: Secure Remote Access and Workload Identity
    [Designing for security and compliance — 3.1] Learners design secure remote access with Identity-Aware Proxy, service account impersonation, Chrome Enterprise Premium and Workload Identity Federation, all of which avoid VPNs and long-lived keys.
33. Level 33: Securing the Software Supply Chain
    [Designing for security and compliance — 3.1] Learners secure the software supply chain using Artifact Registry with vulnerability scanning, build provenance, and Binary Authorization to make sure only trusted images are deployed.
34. Level 34: Securing AI: Model Armor and Sensitive Data
    [Designing for security and compliance — 3.1] Learners secure AI by using Model Armor to screen prompts and responses, Sensitive Data Protection to find and de-identify sensitive data, and secure model deployment practices.
35. Level 35: Designing for Compliance: Regulation, PII, SOC 2
    [Designing for security and compliance — 3.2] Learners design for compliance: health record privacy, children's privacy, data privacy, ownership and data sovereignty; handling credit card data and PII; industry certifications such as SOC 2; and audits supported by retained logs.
36. Level 36: SDLC and CI/CD on Google Cloud
    [Analyzing and optimizing technical and business processes — 4.1] Learners define a software development lifecycle (SDLC) and a continuous integration/continuous deployment process using Cloud Build, Artifact Registry and Cloud Deploy, with environments promoted in stages.
37. Level 37: Testing and Validating Software and Infrastructure
    [Analyzing and optimizing technical and business processes — 4.1] Learners decide where testing and validation of software and infrastructure belong in the delivery process, including policy checks on infrastructure changes and validation before promotion.
38. Level 38: Troubleshooting and Root Cause Analysis
    [Analyzing and optimizing technical and business processes — 4.1] Learners apply best practice for troubleshooting and root cause analysis: forming hypotheses, using logs, metrics and traces, mitigating before fixing, and running blameless postmortems.
39. Level 39: Service Catalog and Self-Service Provisioning
    [Analyzing and optimizing technical and business processes — 4.1] Learners define a service catalogue and provisioning process, so teams can deploy approved, governed templates themselves instead of building infrastructure by hand.
40. Level 40: Disaster Recovery: RTO, RPO and DR Patterns
    [Analyzing and optimizing technical and business processes — 4.1] Learners define disaster recovery processes: setting RTO and RPO, choosing cold, warm or hot DR patterns, using Backup and DR Service, and testing recovery regularly.
41. Level 41: Stakeholder Management and Decision-Making
    [Section 4: Analyzing and optimizing technical and business processes — 4.2] Learn how the exam expects an architect to manage stakeholders through influence and facilitation, and how to run decision-making processes that weigh requirements, options and trade-offs to reach the 'best answer' in scenario questions.
42. Level 42: Change Management, Skills Readiness, Customer Success
    [Section 4: Analyzing and optimizing technical and business processes — 4.2] Learn to plan organisational change management, assess a team's skills readiness for Google Cloud, and apply customer success management so that adoption delivers the promised outcomes.
43. Level 43: CapEx vs OpEx, Resource Optimization, Continuity
    [Section 4: Analyzing and optimizing technical and business processes — 4.2] Learn the business side of cost and resource optimisation: moving from CapEx to OpEx, using committed use discounts, sustained use discounts and rightsizing, and owning business continuity as a business process rather than only a technical disaster recovery plan.
44. Level 44: Advising Teams on Application & Infra Deployment
    [Section 5: Managing implementation — 5.1] Learn how an architect advises development and operations teams on deploying applications and infrastructure: packaging artefacts in Artifact Registry, building with Cloud Build, promoting through environments with Cloud Deploy, and dividing responsibilities between dev and ops.
45. Level 45: API Management Best Practices with Apigee
    [Section 5: Managing implementation — 5.1] Learn API management best practices with Apigee (versioning, security policies, quotas and rate limiting, analytics, developer portals, monetisation) and when the exam prefers Apigee over API Gateway or Cloud Endpoints.
46. Level 46: Advising on Load, Unit and Integration Testing
    [Section 5: Managing implementation — 5.1] Learn to advise teams on testing frameworks: what unit, integration and load tests each prove, where each sits in a CI/CD pipeline, and how to load-test on Google Cloud safely without hitting production quotas.
47. Level 47: Data and System Migration Tooling
    [Section 5: Managing implementation — 5.1] Learn the data and system migration and management tooling the exam expects you to recommend: Migrate to Virtual Machines, Database Migration Service, Storage Transfer Service, Transfer Appliance and BigQuery Data Transfer Service, chosen by data volume, bandwidth and downtime tolerance.
48. Level 48: Cloud Shell, Cloud Code and Gemini Cloud Assist
    [Section 5: Managing implementation — 5.1 and 5.2] Learn how the Cloud Shell Editor, Cloud Shell Terminal and Cloud Code IDE extensions support developers, and how Gemini Cloud Assist helps teams design, deploy, troubleshoot and optimise during implementation.
49. Level 49: gcloud, gsutil, bq and Cloud Emulators
    [Section 5: Managing implementation — 5.2] Learn the Google Cloud SDKs (gcloud, gsutil, which `gcloud storage` is replacing, and bq) and the Cloud Emulators for Bigtable, Spanner, Pub/Sub and Firestore, used for local development and testing without touching cloud resources.
50. Level 50: Terraform IaC, Client Libraries, API Access
    [Section 5: Managing implementation — 5.2] Learn Infrastructure as Code with Terraform (state, modules, plan/apply in pipelines), Google API client libraries, and the best practices for accessing Google APIs: Application Default Credentials, least-privilege service accounts, retries with exponential backoff, and quota awareness.
51. Level 51: Operational Excellence Pillar Principles
    [Section 6: Ensuring solution and operations excellence — 6.1] Learn the principles and recommendations of the operational excellence pillar of the Google Cloud Well-Architected Framework: operational readiness, incident and problem management, managing and optimising cloud resources, automation, and continuous improvement.
52. Level 52: Google Cloud Observability: Monitoring & Logging
    [Section 6: Ensuring solution and operations excellence — 6.2] Learn monitoring and logging with Google Cloud Observability: Cloud Monitoring metrics and dashboards, the Ops Agent, Cloud Logging buckets, log-based metrics, log sinks and routing, and multi-project scoping.
53. Level 53: Profiling and Benchmarking
    [Section 6: Ensuring solution and operations excellence — 6.2] Learn profiling and benchmarking with Cloud Profiler and Cloud Trace: finding CPU and memory hotspots and latency bottlenecks in production, and setting performance baselines to compare releases against.
54. Level 54: Alerting Strategies, SLOs and Error Budgets
    [Section 6: Ensuring solution and operations excellence — 6.2] Learn alerting strategies: alerting on symptoms that users notice rather than on causes, SLI/SLO-based and burn-rate alerts, notification channels, and cutting alert fatigue.
55. Level 55: Deployment and Release Management
    [Section 6: Ensuring solution and operations excellence — 6.3] Learn release strategies (rolling, blue/green, canary, A/B and traffic splitting) on GKE, Cloud Run and managed instance groups, with Cloud Deploy approvals, rollbacks and feature flags.
56. Level 56: Supporting Solutions and Quality Control
    [Section 6: Ensuring solution and operations excellence — 6.4 and 6.5] Learn how to support deployed solutions (incident management, on-call, blameless postmortems, Google Cloud Customer Care levels) and how to evaluate quality control measures such as code review, release gates and production-readiness reviews.
57. Level 57: Chaos Engineering, Pen Testing, Load Testing
    [Section 6: Ensuring solution and operations excellence — 6.6] Learn how to ensure reliability in production with chaos engineering (controlled fault injection), penetration testing (within Google Cloud's acceptable use rules) and production-scale load testing, and how each verifies high-availability designs.
58. Level 58: Case Study: Altostrat Media
    [Case studies — all sections] Work through the Altostrat Media case study (a media and content publishing company using generative AI for engagement, content moderation and summarisation), with its requirements restated in our own words, mapped to Agent Platform, security, storage and operations decisions, and tested with original scenario questions whose distractors are explained.
59. Level 59: Case Study: Cymbal Retail
    [Case studies — all sections] Work through the Cymbal Retail case study (catalogue enrichment with generative AI, conversational commerce and product discovery, and stack modernisation with cost-reduction goals), with requirements restated in our own words and original scenario questions whose distractors are explained.
60. Level 60: Case Study: EHR Healthcare
    [Case studies — all sections] Work through the EHR Healthcare case study (a SaaS electronic health record provider moving off colocation facilities, with rapid growth, high-availability targets, hybrid links to legacy insurer integrations and health-data compliance), with requirements restated in our own words and original scenario questions whose distractors are explained.
61. Level 61: Case Study: KnightMotives Automotive
    [Case studies — all sections] This level works through Google's KnightMotives Automotive case study, restated in our own words: a car maker with a legacy mainframe and ERP, past data breaches, EU rules for autonomous vehicles, weak rural connectivity and dealers with no budget for new equipment, which must modernise the in-vehicle experience across all models within five years. It is practised with original scenario questions whose wrong answers are explained.
62. Level 62: Timed Mock Exam: Professional Cloud Architect Timed mock
    A timed mock exam of 50 questions in 120 minutes, with a 70% pass mark: original scenario, product-comparison and case-study questions across all six exam-guide sections, weighted as the exam weights them, test whether you are ready to sit the Professional Cloud Architect exam.

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