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The official Google Cloud Certified Professional Cloud Architect syllabus, organized by domain. Check off each point as you review it.
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Designing a cloud solution infrastructure that meets business requirements
Product strategy starts from business value: total cost of ownership includes management, so prefer managed and serverless services such as Cloud Run. Ready-made products cover common use cases (Document AI to extract data from documents, Agent Search for semantic search with generative answers), and proofs of concept demonstrate value before investing.
Well-Architected Framework: Cost optimization pillar ↗Migrate to Google Cloud: Assess and discover your workloads ↗
Non-functional requirements (availability, data residency, scalability) determine the deployment archetype: zonal, regional, multi-regional or global. Reliability is defined with user-experience metrics such as request success ratio and latency rather than CPU, and scalability comes from stateless designs that scale horizontally.
Google Cloud deployment archetypes ↗Well-Architected Framework: Reliability pillar ↗
Disaster recovery is part of business continuity and starts with a business impact analysis that sets the RTO (maximum offline time) and RPO (maximum data-loss period). Cold, warm and hot patterns balance cost against recovery speed, and the recovered environment must carry the same security controls as production and be tested regularly.
Disaster recovery planning guide ↗Disaster recovery scenarios for applications ↗
Cost optimization aligns spending with business value and relies on visibility (labels, billing export to BigQuery), matching resources to actual usage and choosing the right discounts: committed use for steady workloads, Spot VMs for preemption-tolerant jobs, and Autoclass or lifecycle rules in Cloud Storage.
To support application design, decouple services with asynchronous messaging (Pub/Sub), use managed caches such as Memorystore for fast reads, design for graceful degradation (throttle and drop excess requests at the frontend) and follow request journeys with Cloud Trace to find bottlenecks.
What is Pub/Sub? ↗Well-Architected Framework: Reliability pillar ↗
Documented patterns exist for integrating external systems: Apigee as an API layer with quotas, analytics and a developer portal; Private Service Connect so other organizations consume a service over internal IPs; Workload Identity Federation for AWS or on-premises workloads without keys; and the handover pattern with Cloud Storage or Pub/Sub.
Data movement is chosen by volume, bandwidth and deadline: gcloud storage for small transfers, Storage Transfer Service for large volumes or from other clouds, and Transfer Appliance when bandwidth is short. Datastream replicates database changes into BigQuery in near real time, and data migrations can use a scheduled window, continuous replication or parallel writes (Y).
Migrate to Google Cloud: Transfer your large datasets ↗Datastream overview ↗
Every design decision balances availability, cost, performance and operational complexity. Deployment archetypes show it: zonal is cheap but fails with the zone, regional survives zone failures, and multi-regional adds replication cost and complexity. Reliability should be just enough for user happiness, with an error budget, and zero-downtime migrations are justified only for critical workloads.
Google Cloud deployment archetypes ↗Well-Architected Framework: Reliability pillar ↗
Each workload gets a strategy: rehost (lift and shift) when it cannot or need not change, refactor or re-architect to use cloud capabilities, rebuild from scratch when it is unsupported or carries technical debt, repurchase a SaaS equivalent, and decommission what the business no longer needs. The more the workload changes, the more time and new skills the migration requires.
Migrate to Google Cloud: Get started ↗Migrate to Google Cloud: Assess and discover your workloads ↗
Success is measured with business-linked indicators: unit economics (cost per order or customer against revenue), a TCO model that includes data center operations and networking for ROI, user-experience and business metrics alongside SLIs, and DORA metrics such as deployment frequency and lead time for changes.
Well-Architected Framework: Cost optimization pillar ↗Migrate to Google Cloud: Assess and discover your workloads ↗
Security and compliance are designed from the requirements: Assured Workloads applies control packages for residency, personnel access and keys to a folder; the resource locations constraint only affects new resources; VPC Service Controls reduces exfiltration even with valid credentials; and Sensitive Data Protection discovers and de-identifies sensitive data.
Assured Workloads overview ↗Overview of VPC Service Controls ↗
Turn business requirements into SLIs, SLOs and an error budget in Cloud Monitoring: the SLO should be stricter than the contractual commitment and never 100%. Choose calendar or rolling periods and request-based or windows-based SLOs according to reporting needs and available metrics. Cloud Trace measures latency across services and Cloud Profiler attributes CPU and memory to code in production.
Concepts in service monitoring ↗Observability in Google Cloud ↗
Designing a cloud solution infrastructure that meets technical requirements
The Well-Architected Framework groups its recommendations into six pillars (operational excellence, security, reliability, cost, performance and sustainability) and cross-pillar perspectives such as AI and ML. Its principles include realistic reliability targets below 100%, graceful degradation under overload, managed services, decoupled and stateless design, and customer responsibility for workload efficiency.
Google Cloud Well-Architected Framework ↗Well-Architected Framework: Reliability pillar ↗
High availability is designed by replicating every critical component across failure domains with automatic failover: Cloud SQL in the HA configuration with synchronous replication across two zones, Bigtable with multi-cluster routing, Hyperdisk Balanced High Availability for stateful volumes, and Cloud DNS failover routing policies with health checks across regions.
Building blocks of reliability in Google Cloud ↗About high availability (Cloud SQL) ↗
Google Cloud lets resources adapt to the workload: Cloud Run scales to zero without traffic, custom machine types match vCPUs and memory (useful with per-CPU licenses), Hyperdisk changes IOPS and throughput while in use without changing size, and Spot VMs cut the cost of fault-tolerant workloads when combined with managed instance groups and checkpoints.
Growth is best handled by scaling out with stateless components: managed instance groups with autoscaling (predictive when load is cyclical and startup is slow), the GKE cluster autoscaler driven by Pod resource requests, shared state in services such as Memorystore, and Spanner when a relational database must scale writes with strong consistency.
Take advantage of horizontal scalability ↗Scaling based on predictions ↗
Latency is reduced by bringing content and compute closer to users: Cloud CDN on the global external Application Load Balancer, backends in several regions behind one anycast IP, and Premium Tier so traffic enters Google's network at the nearest point of presence. Within a zone, compact placement cuts latency between VMs and Local SSD gives the lowest latency for temporary data.
Gemini Cloud Assist is Google Cloud's AI assistant across the lifecycle: natural language design in Application Design Center, investigations that find root causes from logs, metrics and alerts (also triggered by alerting policies), and cost anomaly analysis. In the background it acts through a dedicated agent identity, with least privilege and full audit logging.
Backup design starts from RTO and RPO and the cold, warm or hot pattern each workload needs. Backup and DR stores backups in immutable, indelible vaults with enforced retention that can be locked, and can be managed centrally across projects. Cloud SQL point-in-time recovery always creates a new instance, and scheduled snapshots set the RPO of a disk.
Disaster recovery planning guide ↗Backup vaults for immutable and indelible backups ↗
Designing network, storage, and compute resources
To connect on-premises or other clouds you choose between Cloud VPN (lower cost and bandwidth) and Cloud Interconnect: Dedicated when you can reach a colocation facility, Partner through a service provider, and Cross-Cloud to another cloud; HA VPN over Interconnect adds IPsec without using the internet. Inbound DNS server policies and Private Google Access for on-premises hosts complete the integration.
Choosing a Network Connectivity product ↗Configure Private Google Access for on-premises hosts ↗
Gemini Enterprise Agent Platform brings together Gemini models, Model Garden (Google, partner and open models, with an organization policy to limit which are allowed), RAG grounding to reduce hallucinations, and Agent Runtime with Sessions and Memory Bank for production agents. AI Hypercomputer provides accelerators, open software and consumption options such as Dynamic Workload Scheduler for large training runs.
Shared VPC centralizes subnets, routes and firewalls in a host project and delegates use of specific subnets with compute.networkUser. VPC Network Peering is not transitive and does not allow overlapping ranges; Private Service Connect publishes services to other consumers through their own internal IPs. Hierarchical firewall policies enforce rules projects cannot override, and Cloud NAT provides internet egress without external IPs.
Dataflow runs Apache Beam batch and streaming pipelines as a fully managed, autoscaling service, typically with Pub/Sub for ingestion and BigQuery as the sink. Managed Service for Apache Spark (formerly Dataproc) keeps existing Spark code, on clusters or serverless, and Datastream replicates changes from operational databases into BigQuery with low latency.
Google Cloud offers block storage (Persistent Disk, Hyperdisk, Local SSD), file storage (Filestore, NetApp Volumes, Managed Lustre) and object storage (Cloud Storage). For databases, Bigtable serves single-keyed data at scale with low latency, and Spanner provides relational SQL with horizontal scaling and global consistency. Cloud Storage adds lockable retention and lifecycle rules.
Design an optimal storage strategy for your cloud workload ↗Bigtable overview ↗
Cloud Run is the choice when Google should manage all infrastructure: services handle HTTP and scale to zero, jobs run to completion and functions respond to events. GKE fits when Kubernetes orchestration is needed, and Autopilot mode leaves node management and scaling to Google. Compute Engine is for workloads that need control of the operating system.
Spot VMs use excess capacity at much lower prices but can be preempted at any time, cannot live migrate and have no SLA; they suit fault-tolerant workloads and managed instance groups that recreate them. Custom machine types size vCPUs and memory when no predefined type fits, and TPU workloads run on Compute Engine or GKE.
Creating a migration plan (i.e., documents and architectural diagrams)
To integrate with existing systems, the tiered hybrid pattern moves frontends first and keeps backends on-premises behind an API gateway; Apigee provides that proxy layer with security, quotas and analytics without changing the backend. Cloud bursting uses Cloud Load Balancing with hybrid connectivity NEGs, and Cloud Interconnect links networks without crossing the internet.
Migration Center discovers servers and databases, produces total cost of ownership reports, identifies dependencies and helps plan the migration. To execute it, Migrate to Virtual Machines replicates running VMs with test-clones before cut-over, Database Migration Service runs continuous migrations into Cloud SQL or AlloyDB, and Storage Transfer Service or Transfer Appliance move large datasets.
Migration Center overview ↗Migrate to Google Cloud: Transfer your large datasets ↗
Google's migration guide distinguishes rehost, replatform, refactor, re-architect, rebuild and repurchase, and organizes the journey into assess, plan, deploy and optimize. It recommends stateless, non-critical first movers with few dependencies, validating the plan with proofs of concept, planning DNS TTLs, and preparing a tested rollback for every step.
Migrate to Google Cloud: Get started ↗Migrate to Google Cloud: Best practices for validating a migration plan ↗
Microsoft licenses can be on-demand, billed with compute, or brought through License Mobility. Physical-core licenses with dedicated hardware terms go on sole-tenant nodes. For the financial impact, total cost of ownership must include data center operating and networking costs, and committed use discounts can be resource-based or flexible.
Microsoft Licensing on Google Cloud ↗Bringing your own licenses ↗
Envisioning future solution improvements
After migrating, Google recommends optimizing in a continuous loop of iterations with measurable goals such as SLOs, and assessing team skills before adopting new services. Tools such as Active Assist recommendations, VM rightsizing and the Cloud Billing export to BigQuery help find improvements. The Well-Architected Framework also calls for retrospectives and keeping up with technology.
Migrate to Google Cloud: Optimize your environment ↗What is Active Assist ↗
Business needs change, so designs must anticipate them: scale out before known events and autoscale for unexpected surges, move from vertical scaling to stateless horizontal architectures, and account for the regulations of future markets. The Well-Architected Framework also asks you to keep cloud spending aligned with business value.
Take advantage of elasticity ↗Take advantage of horizontal scalability ↗
A cloud-first design prioritizes managed and serverless services, because total cost of ownership includes patching, monitoring and scaling. It uses loosely coupled microservices that communicate through events, for example with Pub/Sub, idempotent operations that tolerate retries, and stateless, disposable instances that keep data in services such as Cloud Storage.
Align cloud spending with business value ↗What is Microservices Architecture? ↗