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Exam Information
Official specifications published by Google
Exam Format
120 min
50-60
~70% (scaled, pass/fail)
Professional
Registration
$200 USD
Kryterion (onsite), Online proctored, or online proctoring
English, Japanese
Validity
2 years
Retake the standard certification exam within the renewal eligibility period; Renewal eligibility begins 60 days before certification expiration; 50% discount code provided upon initial certification for renewal
GCP-PCD Exam Topics and Domains
GCP-PCD is organized into 4 weighted domains. Expect to work with Cloud Storage, GKE, Cloud Run, Cloud SQL, and more.
1
Designing Highly Scalable, Available, and Reliable Cloud-Native Applications
36%
Designing high-performing applications and APIs
Choosing the appropriate platform based on the use case and requirementsBuilding, refactoring, and deploying application containers to Cloud Run and GKEUnderstanding how Google Cloud services are geographically distributedConfiguring load balancers and applications for session affinity and performant content deliveryImplementing caching solutionsCreating and deploying APIsUsing application rate limiting, authentication, and observabilityIntegrating applications using asynchronous or event-driven approachesOptimizing for cost and resource usageUnderstanding data replication to support zonal and regional failover modelsUsing traffic splitting strategies on a new service on Cloud Run or GKEOrchestrating application services with Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler
- Choose the appropriate compute platform (Compute Engine, GKE, Cloud Run) based on use case and requirements
- Build, refactor, and deploy application containers to Cloud Run and GKE
- Understand geographic distribution of Google Cloud services and its impact on latency
- Configure load balancers for session affinity and content delivery
- Implement caching solutions using Memorystore
- Create and deploy APIs using HTTP REST and gRPC
- Use Apigee and Cloud API Gateway for rate limiting, authentication, and observability
- Integrate applications using asynchronous and event-driven approaches with Eventarc and Pub/Sub
- Optimize applications for cost and resource usage
- Design data replication strategies for zonal and regional failover
- Implement traffic splitting strategies including gradual rollouts, rollbacks, and A/B testing
- Orchestrate services using Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler
Designing secure applications
Implementing data retention and organization policiesUsing security mechanisms that identify vulnerabilities and protect services and resourcesResponding to and resolving vulnerabilitiesStoring, accessing, and rotating application secrets, credentials, and encryption keysAuthenticating to Google Cloud servicesManaging and authenticating end-user accountsSecuring cloud resources using IAM roles for service accountsSecuring service-to-service communicationsRunning services with least privileged accessSecuring application artifacts using Binary Authorization
- Implement data retention and organization policies using Cloud Storage lifecycle management
- Use IAP and Web Security Scanner to protect services and identify vulnerabilities
- Respond to and resolve vulnerabilities identified by Artifact Analysis and Security Command Center
- Store, access, and rotate secrets, credentials, and encryption keys
- Authenticate to Google Cloud services using ADC, JWT, OAuth 2.0, and auth proxies
- Manage and authenticate end-user accounts using Identity Platform
- Secure cloud resources using IAM roles for service accounts
- Secure service-to-service communications with Cloud Service Mesh and Kubernetes Network Policies
- Run services with least privileged access
- Secure application artifacts using Binary Authorization
Storing and accessing data
Selecting the appropriate storage system based on volume and performance requirementsDesigning appropriate schemas for structured and unstructured databasesUnderstanding implications of eventual and strongly consistent replicationCreating signed URLs to grant access to Cloud Storage objectsWriting data to BigQuery for analytics and AI/ML workloads
- Select the appropriate storage system based on data volume and performance requirements
- Design appropriate schemas for structured databases (AlloyDB, Spanner) and unstructured databases (Bigtable, Datastore)
- Understand the implications of eventual and strongly consistent replication across Google Cloud storage services
- Create signed URLs for granting temporary access to Cloud Storage objects
- Write data to BigQuery for analytics and AI/ML workloads
2
Building and Testing Applications
23%
Setting up your development environment
Emulating Google Cloud services using the Google Cloud CLI for local application development and local unit testingUsing Google Cloud developer tools and environments
- Set up local development environments with Google Cloud service emulators
- Use Google Cloud Console, Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations effectively
- Leverage Gemini Cloud Assist and Gemini Code Assist for development tasks
Building
Using Cloud Build and Artifact Registry to build and store containers from source codeConfiguring provenance in Cloud Build
- Use Cloud Build and Artifact Registry to build and store containers from source code
- Configure build provenance for supply chain security with Binary Authorization
Testing
Writing unit tests with the help of Gemini Code AssistExecuting automated integration tests in Cloud Build
- Write unit tests with assistance from Gemini Code Assist
- Execute automated integration tests as part of Cloud Build pipelines
3
Deploying Applications
20%
Deploying applications to Cloud Run
Deploying applications from source codeInvoking Cloud Run services using triggersConfiguring event receiversExposing and securing APIs in applicationsDeploying a new API version in Cloud Endpoints considering backward compatibility
- Deploy applications from source code to Cloud Run
- Invoke Cloud Run services using Eventarc and Pub/Sub triggers
- Configure event receivers for asynchronous processing
- Expose and secure APIs using Apigee
- Deploy new API versions with backward compatibility using Cloud Endpoints
Deploying containers to GKE
Deploying containerized applicationsDefining resource requirements for container workloadsImplementing Kubernetes health checks to increase application availabilityConfiguring the Horizontal Pod Autoscaler for cost optimization
- Deploy containerized applications to GKE
- Define appropriate resource requests and limits for container workloads
- Implement Kubernetes health checks (liveness, readiness, startup probes)
- Configure Horizontal Pod Autoscaler for cost optimization
4
Integrating Applications with Google Cloud Services
21%
Integrating applications with data and storage services
Managing connections to various Google Cloud datastoresReading and writing data to and from various Google Cloud datastoresWriting applications that publish and consume data using Pub/Sub
- Manage connections to various Google Cloud datastores including Cloud SQL, Firestore, and Cloud Storage
- Read and write data to and from Google Cloud datastores using client libraries
- Write applications that publish and consume data using Pub/Sub
Consuming Google Cloud APIs
Enabling Google Cloud servicesMaking API calls by using supported optionsUsing service accounts to make Cloud API calls
- Enable Google Cloud services and APIs
- Make API calls using Cloud Client Libraries, REST API, gRPC, and API Explorer
- Implement batching, pagination, caching, and error handling (exponential backoff) for API calls
- Use service accounts to make authenticated Cloud API calls
Troubleshooting and observability
Instrumenting code to facilitate troubleshooting using metrics, logs, and traces in Google Cloud ObservabilityIdentifying and resolving issues using Google Cloud ObservabilityManaging application issues using Error ReportingUsing trace IDs to correlate trace spans across servicesUsing Gemini Cloud Assist
- Instrument code to produce metrics, logs, and traces using Google Cloud Observability
- Identify and resolve issues using Google Cloud Observability tools
- Manage application issues using Error Reporting
- Use trace IDs to correlate trace spans across distributed services
- Leverage Gemini Cloud Assist for troubleshooting
How do I earn this certification?
Passing GCP-PCD earns the Google Cloud Certified - Professional Cloud Developer certification. It sits in the Developer / Application Development track.
Next Level Options
- GCP-PCA - Professional Cloud Architect
- Fellow - Google Cloud Certified Fellow
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for GCP-PCD is by using the PlanetCert Simulator to practice questions and review detailed explanations.
Who should take this exam?
- 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud
- Experience with cloud-native applications, containerized applications, and APIs
- Proficiency with at least one general-purpose programming language
- Experience with developer tools, orchestration tools, managed services, and serverless platforms
- Familiarity with test strategies and deployment methodologies
- Ability to instrument code to produce metrics, logs, and traces