Free Practice Questions for Google Cloud Professional Cloud Developer Certification

Designing high-performing applications and APIs. Considerations include:

- Choosing the appropriate platform based on the use case and requirements (e.g., Compute Engine, GKE, 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) - Configuring load balancers and applications for session affinity and performant content delivery - Implementing caching solutions (e.g., Memorystore) - Creating and deploying APIs (e.g., HTTP REST, gRPC [Google 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) - 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) - Managing and authenticating end-user accounts (e.g., Identity Platform) - Securing cloud resources using Identity and Access Management (IAM) roles for service accounts - Securing service-to-service communications (e.g., Cloud Service Mesh, Kubernetes Network Policies) - Running services with least privileged access - Securing application artifacts using Binary Authorization

Storing and accessing data. Considerations include:

- 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, Datastore) - 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

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, Gemini Code Assist, Cloud Shell, and Cloud Workstations

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 Gemini Code Assist - Executing automated integration tests in Cloud Build

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) - Exposing and securing APIs in applications (e.g., Apigee) - Deploying a new API version in Cloud Endpoints considering backward compatibility

Deploying containers to GKE. Considerations include:

- Deploying containerized applications - Defining resource requirements for container workloads - Implementing Kubernetes health checks to increase application availability - Configuring the Horizontal Pod Autoscaler for cost optimization

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 datastores - Writing applications that publish and consume data using Pub/Sub

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 - Caching results - Handling errors (e.g., exponential backoff) - 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 Gemini Cloud Assist