Home
Google Cloud
PCA
Syllabus

PCA syllabus

6 domains and 104 topics, updated November 2026

Overview Syllabus Notes Patterns Revision Sample questions Practise

The Professional Cloud Architect exam asks you to turn a company's business and technical requirements into a Google Cloud design, then keep that design secure, cheap and reliable. Many questions refer to one of the published case studies (Altostrat Media, Cymbal Retail, EHR Healthcare, KnightMotives Automotive). This outline lists what each domain expects, in plain terms, following the official exam guide. Tick topics off as you cover them. Your ticks are saved in this browser.

This outline follows the official PCA exam guide (PDF). Check it for the exact wording.

Designing and planning a cloud solution architecture

25%

Turning business and technical requirements into a design, choosing network, storage, compute and AI services, and planning the migration. About 25% of the exam, the largest domain.

Business requirements (1.1)

Business use cases and product strategy
Functional versus non-functional requirements
Business continuity planning
Cost optimization as a design input
Supporting the application design
Integration patterns with external systems
Moving data into and around Google Cloud
Design decision trade-offs
Workload disposition: build, buy, modify or deprecate
Success measures: KPIs, ROI and metrics
Security and compliance requirements
Observability requirements

Technical requirements (1.2)

The Google Cloud Well-Architected Framework and its pillars
High availability and fail-over design
Flexibility of cloud resources
Scaling to meet growth
Performance and latency
Gemini Cloud Assist
Backup and recovery

Network, storage and compute (1.3)

Integration with on-premises and multicloud environments
Google Cloud AI and ML solutions: Gemini models, Agent Builder, Model Garden, AI Hypercomputer
VPC, peering, firewalls, load balancers, routing, container networking, Shared VPC, Private Service Connect
Choosing a data processing service
Choosing object, file and database storage
Mapping workloads to GKE, Cloud Run and Cloud Run functions
Choosing compute: Spot VMs, custom machine types, specialised hardware

Migration planning (1.4)

Integrating the new solution with existing systems
Assessing and migrating systems and data, including Migration Center
Migration methods, workload testing, network planning, dependency planning
Software licence implications and financial impact

Future improvements (1.5)

Adopting new cloud and technology features over time
Evolving business needs
Cloud-first design approach

Read the notes Practise this domain

Managing and provisioning infrastructure

17.5%

Configuring networks, storage and compute for real, and using the Gemini Enterprise Agent Platform (the Vertex AI platform) and Google AI APIs. About 17.5% of the exam.

Network topologies (2.1)

Hybrid connectivity to on-premises: Cloud VPN, Interconnect, Cloud Router
Multicloud and Google Cloud to Google Cloud connectivity
Network security: Cloud NGFW, IDS, Cloud Armor, access control
VPC design and load balancing for internet, private and cloud-adjacent access

Storage systems (2.2)

Allocating storage and sizing it
Data processing and compute provisioning next to the data
Security and access management for data
Data transfer and latency configuration
Retention and lifecycle management
Planning for data growth
Data protection: backup and recovery

Compute systems (2.3)

Provisioning compute resources
Spot versus standard VMs
Networking for Compute Engine, GKE, serverless and VMware Engine
Infrastructure orchestration, configuration and patch management
Container orchestration
Serverless computing

ML workflows on the Agent Platform (2.4)

Pipelines to automate and orchestrate the ML lifecycle
Preparing data for the platform
AI Hypercomputer: GPUs and TPUs for training and serving, consumption models, large-scale training

Prebuilt solutions and APIs (2.5)

Telling the Google AI APIs apart: search, conversation, vision, image, video, audio
Gemini Enterprise features: agents and NotebookLM
Using models from Model Garden

Read the notes Practise this domain

Designing for security and compliance

17.5%

Identity, resource hierarchy, encryption, perimeters, secure access, supply chain and AI security, plus regulation, certifications and audit. About 17.5% of the exam.

Designing for security (3.1)

IAM roles, principals and policies
Organization, folder and project hierarchy
Key management, encryption and secret management
Separation of duties
Audit logs, VPC Service Controls, context-aware access, organization policy, hierarchical firewall policy
Customer-managed encryption keys in Cloud KMS
Secure access: Identity-Aware Proxy, service account impersonation, Chrome Enterprise Premium, Workload Identity Federation
Software supply chain security
AI security: Model Armor, Sensitive Data Protection, secure model deployment

Designing for compliance (3.2)

Law and regulation: health records, children's privacy, data privacy, ownership, data sovereignty
Commercial data rules: payment card data and PII
Industry certifications such as SOC 2
Audits, including log retention and evidence

Read the notes Practise this domain

Analyzing and optimizing processes

15%

Improving technical processes (delivery, testing, DR) and business processes (people, change, cost). About 15% of the exam.

Technical processes (4.1)

Software development lifecycle
Continuous integration and deployment
Troubleshooting and root cause analysis
Testing and validation of software and infrastructure
Service catalog and provisioning
Disaster recovery

Business processes (4.2)

Stakeholder management: influence and facilitation
Change management
Team assessment and skills readiness
Decision-making processes
Customer success management
Cost and resource optimization, CapEx versus OpEx
Business continuity

Read the notes Practise this domain

Managing implementation

12.5%

Advising teams on deployment, APIs, testing and migration tooling, and using Google Cloud programmatically. About 12.5% of the exam.

Advising development and operations (5.1)

Application and infrastructure deployment
API management best practices with Apigee
Load, unit and integration testing frameworks
Data and system migration tooling
Gemini Cloud Assist

Programmatic access (5.2)

Cloud Shell Editor, Cloud Code and Cloud Shell Terminal
SDKs and CLIs: gcloud, gsutil, bq
Emulators for Bigtable, Spanner, Pub/Sub and Firestore
Infrastructure as code with Terraform
Best practices for accessing Google APIs
Google API client libraries

Read the notes Practise this domain

Ensuring solution and operations excellence

12.5%

Operating the design: observability, release management, support and proving reliability. About 12.5% of the exam.

Operational excellence (6.1)

Principles and recommendations of the operational excellence pillar

Observability (6.2)

Monitoring and logging
Profiling and benchmarking
Alerting strategies

Release, support and quality (6.3 to 6.5)

Deployment and release management
Supporting deployed solutions
Evaluating quality control measures

Reliability in production (6.6)

Chaos engineering
Penetration testing
Load testing
SLOs, error budgets and postmortems

Read the notes Practise this domain