Google Professional Cloud Architect
An exam-aligned certification program that trains you to design, provision, secure, and operate scalable, highly available solutions on Google Cloud — and to drive the business processes around them. Built on the official exam guide v6.1 and the Google Cloud Well-Architected Framework, with the published case studies and hands-on labs throughout.
The Google Professional Cloud Architect program is an instructor-led certification course that prepares you for the Google Cloud Certified – Professional Cloud Architect exam. Every module is mapped directly to the official exam guide (v6.1, effective October 30, 2025), so what you practice is exactly what the exam measures.
A Professional Cloud Architect designs, provisions, and operates secure, scalable, highly available solutions on Google Cloud — and drives the technical and business processes around them. This course builds that end-to-end skill set through hands-on labs and the official published case studies (Altostrat Media, Cymbal Retail, EHR Healthcare, and KnightMotives Automotive — several using Google Cloud generative AI solutions), applying the Google Cloud Well-Architected Framework pillars (operational excellence, security, reliability, performance optimization, cost optimization, and sustainability) throughout, and closes with full-length mock exams.
On completion you will be ready to sit the Professional Cloud Architect exam and to take on cloud architect and senior cloud engineering roles.
Exam at a Glance
- Certification: Google Cloud Certified – Professional Cloud Architect | Offered by: Google Cloud
- Level: Professional (no prerequisite certification required)
- Recommended experience: 3+ years in industry, including 1+ years designing and managing solutions using Google Cloud
- Format: 2 hours; multiple choice and multiple select | Result: Pass/fail (no numeric score reported); section-level feedback provided if not passed
- Validity: 2 years; renewable via a shorter renewal exam
- Exam guide basis: Official exam guide v6.1, effective October 30, 2025
- Case studies: Some questions reference a fictitious business — Altostrat Media, Cymbal Retail, EHR Healthcare, KnightMotives Automotive
Exam Sections & Weightings
- Section 1 — Designing and planning a cloud solution architecture — ~25%
- Section 2 — Managing and provisioning a cloud solution infrastructure — ~17.5%
- Section 3 — Designing for security and compliance — ~17.5%
- Section 4 — Analyzing and optimizing technical and business processes — ~15%
- Section 5 — Managing implementation — ~12.5%
- Section 6 — Ensuring solution and operations excellence — ~12.5%
Course Highlights
- 100% aligned to the official exam guide v6.1 (October 30, 2025)
- Case-study driven — all four published case studies worked end-to-end
- Google Cloud Well-Architected Framework pillars applied throughout
- Covers Google Cloud AI/ML: Gemini LLMs, Agent Builder, Model Garden, AI Hypercomputer, Gemini Cloud Assist
- Hands-on with gcloud, Terraform, Cloud Shell, and Google Cloud Observability
- Full-length mock exams with explanations
Learning Outcomes
- Design and plan cloud solution architectures that meet business and technical requirements
- Provision and manage networks, storage, and compute on Google Cloud
- Design secure, compliant architectures with IAM, KMS, and VPC Service Controls
- Analyze and optimize technical and business processes (SDLC, CI/CD, DR)
- Manage implementations programmatically with SDKs, Terraform, and emulators
- Pass the Professional Cloud Architect exam with confidence
Job Opportunities
- Cloud Solutions Architect
- Google Cloud Architect
- Cloud Infrastructure Engineer
- Platform Engineer
- Enterprise Architect
- Cloud Consultant
Material Includes
- Class recordings
- Lab guides & case study workbooks
- Mock exams
- Certificate
Who Can Join
- IT Professionals & Cloud Engineers
- Developers & DevOps Engineers
- Aspiring Cloud Architects
Estimated Time: 12–13 Hours
1.1 Design infrastructure meeting business requirements- Business use cases & product strategy; functional/non-functional requirements
- Business continuity; cost optimization; application design support
- Integration patterns; data movement; design trade-offs
- Workload disposition (build/buy/modify/deprecate); success measures (KPI, ROI, metrics)
- Security & compliance; observability
- Well-Architected Framework; high availability & failover
- Resource flexibility; scalability; performance & latency
- Gemini Cloud Assist; backup and recovery
- On-premises/multicloud integration
- Google Cloud AI/ML: Gemini LLMs, Agent Builder, Model Garden, AI Hypercomputer
- Cloud-native networking: VPC, peering, firewalls, load balancers, Private Service Connect
- Data processing solutions; storage types (object/file/database)
- Mapping compute to platform products (GKE, Cloud Run, Cloud Run functions); compute resources (spot VMs, custom machine types)
- Integrating with existing systems; assessing & migrating systems and data (Migration Center)
- Migration methodologies, testing, network & dependency planning
- Software license & financial implications
- Cloud/technology improvements; evolving business needs
- Cloud-first design approach
- Design a full solution architecture for a published case study
- Build a migration plan with Migration Center
Estimated Time: 8–9 Hours
2.1 Configure network topologies- Hybrid & multicloud networking
- Security protection (intrusion protection, access control, firewalls)
- VPC design and load balancing
- Data allocation; processing & compute provisioning
- Security & access; data transfer & latency
- Retention & lifecycle management; growth planning
- Data protection (backup & recovery)
- Compute provisioning; volatility (spot vs. standard)
- Cloud-native compute networking (Compute Engine, GKE, serverless, VMware Engine)
- Infrastructure orchestration & patch management; container orchestration; serverless
- Agent Platform Pipelines for the ML lifecycle
- Data integration
- AI Hypercomputer: GPUs/TPUs for training & serving, consumption-model optimization, large-scale training
- Google AI APIs (Search, Conversation, Vision, Image, Video, Audio)
- Gemini Enterprise features (AI Agents, NotebookLM)
- Integrating Model Garden models
- Provision a hybrid VPC topology with load balancing
- Deploy compute across GKE, Cloud Run, and spot VMs
Estimated Time: 8–9 Hours
3.1 Design for security- IAM; resource hierarchy (organizations, folders, projects)
- Data security: key management, encryption, secret management
- Separation of duties; security controls (auditing, VPC Service Controls, context-aware access, org policy, hierarchical firewall)
- Cloud KMS / customer-managed keys
- Secure remote access: Identity-Aware Proxy, service account impersonation, Chrome Enterprise Premium, Workload Identity Federation
- Securing the software supply chain
- Securing AI: Model Armor, Sensitive Data Protection, secure model deployment
- Legislation & regulation: health/children's/data privacy, ownership, data sovereignty
- Commercial: credit card data, PII
- Industry certifications (e.g., SOC 2)
- Audits and logs
- Design a resource hierarchy with IAM, org policy, and VPC Service Controls
- Configure CMEK encryption and Identity-Aware Proxy access
Estimated Time: 6–7 Hours
4.1 Analyze and define technical processes- SDLC; CI/CD
- Troubleshooting & root cause analysis
- Testing & validation of software and infrastructure
- Service catalog & provisioning
- Disaster recovery
- Stakeholder management; change management
- Team assessment/skills readiness; decision-making
- Customer success management
- Cost/resource optimization (CapEx/OpEx)
- Business continuity
- Design a CI/CD + DR process for a case-study workload
- Run a stakeholder/change-management exercise for a migration
Estimated Time: 5–6 Hours
5.1 Advise development and operations teams- Application & infrastructure deployment
- API management best practices (Apigee)
- Testing frameworks (load/unit/integration)
- Data & system migration tooling
- Gemini Cloud Assist
- Cloud Shell Editor, Cloud Code, Cloud Shell Terminal
- Google Cloud SDKs (gcloud, gsutil, bq)
- Cloud Emulators (Bigtable, Spanner, Pub/Sub, Firestore)
- Infrastructure as Code (Terraform)
- Google API best practices and client libraries
- Deploy infrastructure with Terraform and the gcloud SDK
- Develop against Cloud Emulators from Cloud Shell
Estimated Time: 5–6 Hours
Topics:- Operational excellence pillar of the Well-Architected Framework — principles & recommendations
- Google Cloud Observability: monitoring & logging, profiling & benchmarking, alerting strategies
- Deployment and release management
- Supporting deployed solutions
- Evaluating quality control measures
- Ensuring production reliability: chaos engineering, penetration testing, load testing
- Build monitoring dashboards and alerting policies with Google Cloud Observability
- Run a load test and a reliability (chaos) drill against a deployed service
Estimated Time: 3–4 Hours
Learning Objectives:- Master case-study questions and time management for the 2-hour exam
- Review all four published case studies against the exam sections
- Case-study strategy: Altostrat Media, Cymbal Retail, EHR Healthcare, KnightMotives Automotive
- Section-by-section exam strategy and elimination techniques
- Renewal path: 2-year validity and the shorter renewal exam
- Full-length timed mock exams with detailed review
- Capstone: end-to-end architecture for one case study
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