PCD syllabus: every exam domain and topic

Designing cloud-native applications

32%

Designing scalable, secure and reliable applications: platform choice, networking and caching, APIs, asynchronous design, security controls and data storage. About 32% of the exam.

Platform choice and containers

Choosing between Compute Engine, GKE and Cloud Run for a use case and its requirements
Building, refactoring and deploying containers to Cloud Run and GKE
Defining CPU and memory requirements for workloads
Optimizing cost and resource usage

Geography, load balancing and caching

How services are distributed: zonal, regional, multi-regional and global, and the latency effect
Matching load balancer types to use cases
Session affinity for performant content delivery
Caching with Memorystore
Data replication for zonal and regional failover

APIs and application integration

Creating and deploying REST and gRPC APIs
Rate limiting, authentication and observability with Apigee or API Gateway
Asynchronous and event-driven integration with Eventarc and Pub/Sub
Orchestrating services with Workflows, Eventarc, Cloud Tasks and Cloud Scheduler
Traffic splitting: gradual rollouts, rollbacks and A/B tests on Cloud Run or GKE

Retention, vulnerabilities and secrets

Data retention with Cloud Storage lifecycle rules and retention policies with bucket lock
Finding vulnerabilities and protecting services with IAP and Web Security Scanner
Responding to findings from Artifact Analysis and Security Command Center
Storing, accessing and rotating secrets, credentials and keys with Secret Manager, Cloud KMS and Workload Identity Federation

Identity, service communication and supply chain

Authenticating with Application Default Credentials, JWT, OAuth 2.0, the Cloud SQL and AlloyDB Auth Proxies, Identity Platform and Workload Identity Federation
IAM roles for service accounts and least privilege access
Secure service-to-service communication: Cloud Service Mesh, Kubernetes network policies, Direct VPC egress, Private Service Connect
Securing artifacts with Binary Authorization

Storing and accessing data

Selecting a storage system for data volume and performance needs
Schema design for AlloyDB, Spanner, Bigtable and Firestore
Eventual versus strong consistency in AlloyDB, Bigtable, Cloud SQL, Spanner and Cloud Storage
Signed URLs for Cloud Storage objects
Writing data to BigQuery for analytics and AI/ML workloads

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Building and testing applications

23%

The developer workflow: local environment and tooling, building and storing containers, build provenance, and automated testing. About 23% of the exam.

Development environment

Emulating Google Cloud services locally for development and unit tests
Using the console, Cloud SDK, Cloud Code, Gemini Cloud Assist, Cloud Shell and Cloud Workstations
Configuring IDEs with the Cloud SDK and AI tooling such as coding assistants and MCP servers

Building

Building and storing containers from source with Cloud Build and Artifact Registry
Configuring provenance in Cloud Build and enforcing it with Binary Authorization

Testing

Writing unit tests with AI coding assistants and reviewing the output
Running automated integration tests in Cloud Build

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Deploying applications

24%

Configuring applications for deployment to Cloud Run and GKE, including triggers, API exposure, probes and autoscaling. About 24% of the exam.

Cloud Run

Deploying from source code
Invoking services with triggers from Eventarc and Pub/Sub
Configuring event receivers
Versioning, exposing and securing APIs, for example with Apigee

GKE

Deploying containerized applications
Kubernetes health checks (startup, readiness, liveness) for availability
Horizontal Pod Autoscaler attributes: scaling and metrics

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Integrating Google Cloud services

21%

Application code that connects to data stores, messaging and Google Cloud APIs, and can be troubleshot in production. About 21% of the exam.