The Google GCP-PCD exam preparation guide is designed to provide candidates with necessary information about the Professional Cloud Developer exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Google Cloud Platform - Professional Cloud Developer (GCP-PCD) exam.
It is recommended for all the candidates to refer the GCP-PCD objectives and sample questions provided in this preparation guide. The Google Professional Cloud Developer certification is mainly targeted to the candidates who want to build their career in Professinoal domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Google Professional Cloud Developer exam.
Google GCP-PCD Exam Summary:
| Exam Name | Google Professional Cloud Developer |
| Exam Code | GCP-PCD |
| Exam Price | $200 USD |
| Duration | 120 minutes |
| Number of Questions | 50-60 |
| Passing Score | Pass / Fail (Approx 70%) |
| Recommended Training / Books |
Google Cloud training Google Cloud documentation Google Cloud solutions |
| Schedule Exam | Google CertMetrics |
| Sample Questions | Google GCP-PCD Sample Questions |
| Recommended Practice | Google Cloud Platform - Professional Cloud Developer (GCP-PCD) Practice Test |
Google Professional Cloud Developer Syllabus:
| Section | Objectives |
|---|---|
Designing highly scalable, secure, and reliable cloud-native applications (~32% of the exam) |
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Designing high-performing applications and APIs. Considerations include:
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- Choosing the appropriate platform based on the use case and requirements (e.g., Compute Engine, Google Kubernetes Engine, Cloud Run)
- Building, refactoring, and deploying application containers to Cloud Run and GKE - Understanding how Google Cloud services are geographically distributed (e.g., latency, regional services, zonal services) - Understanding the use cases for load balancers - Enabling session affinity for performant content delivery - Implementing caching solutions (e.g., Memorystore) - Creating and deploying APIs (e.g., HTTP REST, gRPC [Remote Procedure Call]) - Using application rate limiting, authentication, and observability (e.g., Apigee, Cloud API Gateway) - Integrating applications using asynchronous or event-driven approaches (e.g., Eventarc, Pub/Sub) - Defining resource requirements for workloads - Optimizing for cost and resource usage - Understanding data replication to support zonal and regional failover models - Using traffic splitting strategies (e.g., gradual rollouts, rollbacks, A/B testing) on a new service on Cloud Run or GKE - Orchestrating application services with Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler |
| Designing secure applications. Considerations include: |
- Implementing data retention and organization policies (e.g., Cloud Storage Object Lifecycle Management, Cloud Storage use and lock retention policies)
- Using security mechanisms that identify vulnerabilities and protect services and resources (e.g., Identity-Aware Proxy [IAP], Web Security Scanner) - Responding to and resolving vulnerabilities, including those identified by Artifact Analysis and Security Command Center - Storing, accessing, and rotating application secrets, credentials, and encryption keys (e.g., Secret Manager, Cloud Key Management Service, Workload Identity Federation) - Authenticating to Google Cloud services (e.g., Application Default Credentials, JSON Web Token [JWT], OAuth 2.0, Cloud SQL Auth Proxy, AlloyDB Auth Proxy, Identity Platform, WIF) - Securing cloud resources using Identity and Access Management (IAM) roles for service accounts - Incorporating secure service-to-service communications (e.g., Cloud Service Mesh, Kubernetes Network Policies, Direct VPC egress, private service connectivity) - Running services with least privileged access - Securing application artifacts using Binary Authorization |
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Storing and accessing data. Considerations include:
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- Selecting the appropriate storage system based on the volume of data and performance requirements
- Designing appropriate schemas for structured databases (e.g., AlloyDB, Spanner) and unstructured databases (e.g., Bigtable, Firestore) - Understanding the implications of eventual and strongly consistent replication of AlloyDB, Bigtable, Cloud SQL, Spanner, and Cloud Storage - Creating signed URLs to grant access to Cloud Storage objects - Writing data to BigQuery for analytics and AI/ML workloads |
Building and testing applications (~23% of the exam) |
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| Setting up your development environment. Considerations include: |
- Emulating Google Cloud services using the Google Cloud CLI for local application development and local unit testing - Using the Google Cloud console, Cloud SDK, Cloud Code, Gemini Cloud Assist, Cloud Shell, and Cloud Workstations - Configuring IDEs with the appropriate integrations (e.g., Cloud SDK, AI tooling [coding
assistants, MCP servers])
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| Building. Considerations include: |
- Using Cloud Build and Artifact Registry to build and store containers from source code - Configuring provenance in Cloud Build (e.g., Binary Authorization) |
| Testing. Considerations include: |
- Writing unit tests with the help of AI coding assistants |
Configuring cloud-native applications for deployment (~24% of the exam) |
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| Deploying applications to Cloud Run. Considerations include: |
- Deploying applications from source code - Invoking Cloud Run services using triggers (e.g., Eventarc, Pub/Sub) - Configuring event receivers (e.g., Eventarc, Pub/Sub) - Versioning, exposing and securing APIs in applications (e.g., Apigee) |
| Deploying containers to GKE. Considerations include: |
- Deploying containerized applications - Implementing Kubernetes health checks to increase application availability - Incorporating Horizontal Pod Autoscaler attributes (scaling, metrics) |
Integrating applications with Google Cloud services (~21% of the exam) |
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| Integrating applications with data and storage services. Considerations include: |
- Managing connections to various Google Cloud datastores (e.g., Cloud SQL, Firestore, Cloud Storage) - Reading and writing data to and from various Google Cloud data sources - Writing applications that publish and consume data using messaging services |
| Consuming Google Cloud APIs. Considerations include: |
- Enabling Google Cloud services - Making API calls by using supported options (e.g., Cloud Client Libraries, REST API, gRPC, API Explorer) taking into consideration:
- Using service accounts to make Cloud API calls |
| Troubleshooting and observability. Considerations include: |
- Instrumenting code to facilitate troubleshooting using metrics, logs, and traces in Google Cloud Observability - Identifying and resolving issues using Google Cloud Observability - Managing application issues using Error Reporting - Using trace IDs to correlate trace spans across services - Using AI-assisted observability |
