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Google Cloud Exam Syllabus

Professional Cloud Developer syllabus, skills measured, and exam topics

A Professional Cloud Developer builds and configures scalable, secure applications by using Google Cloud-recommended tools and best practices. They are proficient in the full development lifecycle, from architecting cloud-native applications to integrating advanced machine learning

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Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
Section 2: Building and testing applications 23%
Section 3: Configuring cloud-native applications for deployment 24%
Section 4: Integrating applications with Google Cloud services 21%

What to know before you study

These sections explain the role, audience, and exam framing behind the outline.

Section 1: Designing highly scalable, secure, and reliable cloud-native

  • applications (~32% of the exam)
  • 1.1 Designing high-performing applications and APIs. Considerations include:
  • 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

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

Section 2: Building and testing applications (~23% of the exam)

  • 2.1 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])
  • 2.2 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)
  • 2.3 Testing. Considerations include:
  • Writing unit tests with the help of AI coding assistants

Section 3: Configuring cloud-native applications for deployment (~24% of the

  • 3.1 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)
  • 3.2 Deploying containers to GKE. Considerations include:
  • Deploying containerized applications
  • Implementing Kubernetes health checks to increase application availability
  • Incorporating Horizontal Pod Autoscaler attributes (scaling, metrics)

Section 4: Integrating applications with Google Cloud services (~21% of the exam)

  • 4.1 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
  • 4.2 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:
  • ○ Batching requests
  • ○ Restricting return data
  • ○ Paginating results