Google GCP-PCA (Professional Cloud Architect) Certification Exam Syllabus

• Business use cases and product strategy
• Cost optimization
• Supporting the application design
• Integration with external systems
• Movement of data
• Design decision trade-offs
• Build, buy, modify, or deprecate
• Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], metrics)
• Compliance and observability

2. Designing a solution infrastructure that meets technical requirements. Considerations include:

• High availability and failover design
• Elasticity of cloud resources with respect to quotas and limits
• Scalability to meet growth requirements
• Performance and latency

3. Designing network, storage, and compute resources. Considerations include:

• Integration with on-premises/multicloud environments
• Cloud-native networking (VPC, peering, firewalls, container networking)
• Choosing data processing technologies
• Choosing appropriate storage types (e.g., object, file, databases)
• Choosing compute resources (e.g., preemptible, custom machine type, specialized workload)
• Mapping compute needs to platform products

4. Creating a migration plan (i.e., documents and architectural diagrams). Considerations include:

• Integrating solutions with existing systems
• Migrating systems and data to support the solution
• Software license mapping
• Network planning
• Testing and proofs of concept
• Dependency management planning

5. Envisioning future solution improvements. Considerations include:

• Cloud and technology improvements
• Evolution of business needs
• Evangelism and advocacy

• Extending to on-premises environments (hybrid networking)
• Extending to a multicloud environment that may include Google Cloud to Google Cloud communication
• Security protection (e.g. intrusion protection, access control, firewalls)

2. Configuring individual storage systems. Considerations include:

• Data storage allocation
• Data processing/compute provisioning
• Security and access management
• Network configuration for data transfer and latency
• Data retention and data life cycle management
• Data growth planning

3. Configuring compute systems. Considerations include:

• Compute resource provisioning
• Compute volatility configuration (preemptible vs. standard)
• Network configuration for compute resources (Google Compute Engine, Google Kubernetes Engine, serverless networking)
• Infrastructure orchestration, resource configuration, and patch management
• Container orchestration

• Identity and access management (IAM)
• Resource hierarchy (organizations, folders, projects)
• Data security (key management, encryption, secret management)
• Separation of duties (SoD)
• Security controls (e.g., auditing, VPC Service Controls, context aware access, organization policy)
• Managing customer-managed encryption keys with Cloud Key Management Service
• Remote access

2. Designing for compliance. Considerations include:

• Legislation (e.g., health record privacy, children’s privacy, data privacy, and ownership)
• Commercial (e.g., sensitive data such as credit card information handling, personally identifiable information [PII])
• Industry certifications (e.g., SOC 2)
• Audits (including logs)

• Software development life cycle (SDLC)
• Continuous integration / continuous deployment
• Troubleshooting / root cause analysis best practices
• Testing and validation of software and infrastructure
• Service catalog and provisioning
• Business continuity and disaster recovery

2. Analyzing and defining business processes. Considerations include:

• Stakeholder management (e.g. influencing and facilitation)
• Change management
• Team assessment / skills readiness
• Decision-making processes
• Customer success management
• Cost optimization / resource optimization (capex / opex)

3. Developing procedures to ensure reliability of solutions in production (e.g., chaos engineering, penetration testing)

• Application development
• API best practices
• Testing frameworks (load/unit/integration)
• Data and system migration and management tooling

2. Interacting with Google Cloud programmatically. Considerations include:

• Google Cloud Shell
• Google Cloud SDK (gcloud, gsutil and bq)
• Cloud Emulators (e.g. Cloud Bigtable, Datastore, Spanner, Pub/Sub, Firestore)