Google Cloud Professional Cloud DeveloperStudy guide
The professional certification for building scalable, secure cloud-native applications with Google-recommended tools and best practices (Professional Cloud Developer).
About Google Cloud Professional Cloud Developer (GCP-PCD)
Google Cloud Professional Cloud Developer (GCP-PCD) is a Professional / Expert-level certification from Google Cloud. This page organizes the exam scope into a 6-chapter, 12-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Designing highly scalable, secure, and reliable cloud-native applications~32%
- Building and testing applications~23%
- Configuring cloud-native applications for deployment~24%
- Integrating applications with Google Cloud services~21%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://cloud.google.com/learn/certification/cloud-developer
1Designing cloud-native applications
- 1.1Designing high-performing applications and APIs
Understand choosing platforms by use case (Compute Engine, GKE, Cloud Run), load balancers and session affinity, caching (Memorystore), creating APIs (REST, gRPC) and managing them (Apigee, API Gateway), event-driven integration (Pub/Sub, Eventarc), and traffic splitting for gradual rollouts.
- 1.2Designing secure applications
Understand managing secrets and keys (Secret Manager, Cloud KMS), authentication via service accounts and Workload Identity Federation, least-privilege IAM, protection with Identity-Aware Proxy (IAP), artifact verification with Binary Authorization, and secure service-to-service communication.
2Designing data, reliability, and cost
- 2.1Storing and accessing data
Understand choosing storage by data volume and performance/consistency requirements (AlloyDB, Spanner, Bigtable, Firestore, Cloud SQL, Cloud Storage, BigQuery), schema design for structured/unstructured data, eventual vs strong consistency, Cloud Storage signed URLs, and writing to BigQuery for analytics and AI/ML.
- 2.2Designing availability, resilience, and cost
Understand geographic distribution and latency across regions/zones, data replication for zonal/regional failover, defining workload resource requirements, optimizing cost and resource usage, and designing reliability via idempotency and retries (exponential backoff).
3Building and testing applications
- 3.1Setting up the development environment
Understand emulating services with the Google Cloud CLI for local development and testing, using Cloud Code, Cloud Shell, Cloud Workstations, and Gemini Cloud Assist, IDE integrations, and configuring environments that boost developer productivity.
- 3.2Building and testing (CI)
Understand building containers from source with Cloud Build, storing them in Artifact Registry, ensuring provenance with Binary Authorization, automating unit/integration tests, and continuous validation in CI pipelines.
4Configuring applications for deployment
- 4.1Deploying to Cloud Run
Understand deploying to Cloud Run from source, rollouts via revisions and traffic splitting, configuring triggers (Eventarc, Pub/Sub) and event receivers, and versioning/exposing/securing APIs with Apigee.
- 4.2Deploying to GKE
Understand deploying containerized apps to GKE (Deployments, Services), Kubernetes health checks (liveness/readiness probes) for availability, scaling with the Horizontal Pod Autoscaler, and choosing between Cloud Run and GKE.
5Integrating with data and APIs
- 5.1Integrating data, storage, and messaging
Understand managing connections to datastores (Cloud SQL, Firestore, Cloud Storage), reading/writing across data sources, publishing/consuming data with Pub/Sub, and connection security/pooling/auth (e.g., Cloud SQL Auth Proxy).
- 5.2Consuming Google Cloud APIs
Understand enabling Google Cloud services, calling APIs via client libraries/REST/gRPC, batching/restricting results/pagination/caching, error handling with exponential backoff, and making API calls with service accounts.
6Observability and troubleshooting
- 6.1Observability (metrics, logs, traces)
Understand instrumenting code with Google Cloud Observability, collecting metrics/logs/traces, investigating with Cloud Logging, distributed tracing and correlating spans by trace ID with Cloud Trace, and AI-assisted observability.
- 6.2Troubleshooting and error handling
Understand aggregating and managing application errors with Error Reporting, identifying and resolving issues with Google Cloud Observability, the debugging workflow, and addressing latency and error-rate problems.