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:
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Exam Name
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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 | PEARSON VUE |
| 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, available, and reliable cloud-native applications (27% of the exam) |
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| Designing high-performing applications and APIs. Considerations include: |
- Microservices - Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service), CaaS (container as a service), PaaS (platform as a service), and FaaS (function as a service) - Understanding how Google Cloud services are geographically distributed (e.g., latency, regional services, zonal services) - User session management - Caching solutions - HTTP REST versus gRPC (Google Remote Procedure Call) - Designing API services with API Gateway and Cloud Endpoints - Loosely coupled asynchronous applications (e.g., Apache Kafka, Pub/Sub, Eventarc) - Instrumenting code to produce metrics, logs, and traces - Graceful shutdown of applications on platform termination - Writing fault-tolerant code |
| Designing secure applications. Considerations include: |
- Implementing data lifecycle and residency requirements relevant for applicable regulations - Security mechanisms that protect services and resources - Security mechanisms that secure/scan application binaries and manifests - Storing, accessing, and rotating application secrets and keys (e.g., Secret Manager, Cloud Key Management Service) - Authenticating to Google Cloud services (e.g., application default credentials, JSON Web Token (JWT), OAuth 2.0) - End-user account management and authentication using Identity Platform - IAM roles for users, groups, and service accounts - Securing service-to-service communications (e.g., service mesh, Kubernetes Network Policies, and Kubernetes namespaces) - Running services with least privileged access (e.g., Workload Identity) - Certificate-based authentication (e.g., SSL, mTLS) |
| Managing application data. Considerations include: |
- Defining database schemas for Google-managed databases (e.g., Firestore, Cloud Spanner, Bigtable, Cloud SQL)
|
Building and testing applications (20% of the exam) |
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| Setting up your local development environment. Considerations include: |
- Emulating Google Cloud services for local application development - Using the Google Cloud Console, Google Cloud SDK, and Cloud Shell tools - Using developer tooling (e.g., Cloud Code, Skaffold) |
| Building. Considerations include: |
- Source control management - Creating secure container images from code - Developing a continuous integration pipeline using services (e.g., Cloud Build, Artifact Registry) that construct deployment artifacts - Code and test build optimization |
| Testing. Considerations include: |
- Unit testing (e.g., emulators) - Integration testing - Performance testing - Load testing - Failure testing/chaos engineering |
Deploying applications (18% of the exam) |
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| Adopting appropriate feature rollout strategies. Considerations include: |
- A/B testing - Feature flags - Backward compatibility |
| Deploying applications to a serverless computing environment. Considerations include: |
- Sizing and scaling serverless environments - Deploying from source code - Invocation via triggers - Configuring event receivers - Exposing and securing application APIs (e.g., API Gateway, Cloud Endpoints) |
| Deploying applications and services to Google Kubernetes Engine (GKE). Considerations include: |
- Deploying a containerized application to GKE - Integrating Kubernetes RBAC with Identity and Access Management (IAM) - Configuring Kubernetes namespaces - Defining workload specifications (e.g., resource requirements) - Building a container image using Cloud Build - Configuring application accessibility to user traffic and other services - Managing container lifecycle |
Integrating Google Cloud services (20% of the exam) |
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| Integrating an application with data and storage services. Considerations include: |
- Managing connections to data stores (e.g., Cloud SQL, Cloud Spanner, Firestore, Bigtable, Cloud Storage) - Reading/writing data to/from various data stores - Writing an application that publishes/consumes data asynchronously (e.g., from Pub/Sub) |
| Integrating an application with compute services. Considerations include: |
- Using service discovery (e.g., Service Directory) - Reading instance metadata to obtain application configuration - Graceful application startup and shutdown |
| Integrating Cloud APIs with applications. Considerations include: |
- Enabling a Cloud API - Making API calls using supported options (e.g., Cloud Client Library, REST API or gRPC, API Explorer) taking into consideration:
- Using service accounts to make Cloud API calls |
Managing deployed applications (15% of the exam) |
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| Managing cloud compute services (e.g., Google Kubernetes Engine, serverless). Considerations include: |
- Analyzing lifecycle events - Using external metrics and corresponding alerts - Configuring workload autoscaling |
| Troubleshooting applications. Considerations include: |
- Using Debugger - Using Cloud Logging - Using Cloud Monitoring - Using Cloud Profiler - Using Cloud Trace - Using Error Reporting - Using documentation, forums, and Google Cloud support |