Course Summary
Professional Cloud Developer
A Professional Cloud Developer builds and deploys scalable, secure, and highly available applications by using Google-recommended tools and best practices. This individual has experience with cloud-native applications, containerized applications, APIs, developer tools, orchestration tools, managed services, test strategies, serverless platforms, and next-generation databases. This individual also has proficiency with at least one general-purpose programming language and instruments their code to produce metrics, logs, and traces.
The Professional Cloud Developer exam assesses your ability to:
Design highly scalable, available, reliable cloud-native applications
Build and test applications
Deploy applications
Integrate an application with Google Cloud services
Section 1: Designing highly scalable, available, and reliable cloud-native applications
1.1 Designing high-performing applications and APIs. Considerations include:
● Microservices architecture
● Choosing the appropriate platform based on the use case and requirements (e.g., IaaS [infrastructure as a service], CaaS [container as a service], PaaS [platform as a service], FaaS [function as a service])
● Application modernization (e.g., containerization)
● 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)
● Incorporating Service Control capabilities offered by API services (e.g. Apigee)
● Loosely coupled asynchronous applications (e.g., Apache Kafka, Pub/Sub, Eventarc)
● Instrumenting code to produce metrics, logs, and traces
● Cost optimization and resource optimization
● Graceful handling of errors, disasters, and scaling events
1.2 Designing secure applications. Considerations include:
● Implementing data lifecycle and residency for applicable regulatory requirements
● Security mechanisms that identify vulnerabilities and protect services and resources (e.g., Identity-Aware Proxy [IAP], Web Security Scanner)
● Security mechanisms that secure/scan application binaries, dependencies, and manifests (e.g., Container Analysis)
● Storing, accessing, and rotating application secrets and encryption 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 by using Identity Platform
● Identity and Access Management (IAM) roles for users, groups, and service accounts
● Securing service-to-service communications (e.g., service mesh, Kubernetes Network Policies, Kubernetes namespaces)
● Running services with keyless and least privileged access (e.g., Workload Identity, Workload identity federation)
● Certificate-based authentication (e.g., SSL, mTLS)
● Supply-chain Levels for Software Artifacts (SLSA)
1.3 Choosing storage options for application data. Considerations include:
● Time-limited access to objects
● Data retention requirements
● Structured versus unstructured data (e.g., SQL versus NoSQL)
● Strong versus eventual consistency
● Data volume
● Data access patterns
● Online transaction processing (OLTP) versus data warehousing
Section 2: Building and testing applications
2.1 Setting up your local development environment. Considerations include:
● Emulating Google Cloud services for local application development
● Using the Google Cloud console, Google Cloud SDK, Cloud Shell, and Cloud Workstations
● Using developer tooling (e.g., common IDEs, Cloud Code, Skaffold)
● Authenticating to Google Cloud services (e.g., Cloud SQL Auth proxy, AlloyDB Auth proxy)
2.2 Building. Considerations include:
● Source control management
● Creating secure container images from code
● Developing a continuous integration pipeline by using services (e.g., Cloud Build, Artifact Registry) that construct deployment artifacts
● Code and test build optimization
2.3 Testing. Considerations include:
● Unit testing
● Integration testing including the use of emulators
● Performance testing
● Load testing
● Failure testing/chaos engineering
Section 3: Deploying applications
3.1 Adopting appropriate feature rollout strategies. Considerations include:
● A/B testing
● Feature flags
● Backward compatibility
● Versioning APIs (e.g., Apigee)
3.2 Deploying applications to a serverless computing environment. Considerations include:
● Deploying applications from source code
● Using triggers to invoke functions
● Configuring event receivers (e.g., Eventarc, Pub/Sub)
● Exposing and securing application APIs (e.g., Apigee)
3.3 Deploying applications and services to Google Kubernetes Engine (GKE). Considerations include:
● Deploying a containerized application to GKE
● Integrating Kubernetes role-based access control (RBAC) with IAM
● Defining workload specifications (e.g., resource requirements)
● Building a container image by using Cloud Build
Section 4: Integrating an application with Google Cloud services
4.1 Integrating an application with data and storage services. Considerations include:
● Managing connections to datastores (e.g., Cloud SQL, Firestore, Bigtable, Cloud Storage)
● Reading/writing data to or from various datastores
● Writing an application that publishes or consumes data asynchronously (e.g., from Pub/Sub or streaming data sources)
● Orchestrate application services with Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler
4.2 Integrating an application with Google Cloud APIs. Considerations include:
● Enabling Google Cloud services
● Making API calls by using supported options (e.g., Cloud Client Library, REST API or gRPC, API Explorer) taking into consideration:
○ Batching requests
○ Restricting return data
○ Paginating results
○ Caching results
○ Error handling (e.g., exponential backoff)
● Using service accounts to make Cloud API calls
● Integrating with Google Cloud’s operations suite
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