Google Cloud Professional Cloud Developer — Simulado e guia de estudo

Designing high-performing applications and APIs including choosing the appropriate platform based on use case and requirements (Compute Engine, GKE, Cloud Run), building, refactoring, and deploying application containers to Cloud Run and GKE, understanding how Google Cloud services are geographically distributed (latency, regional services, zonal services), configuring load balancers and applications for session affinity and performant content delivery, implementing caching solutions (Memorystore), creating and deploying APIs (HTTP REST, gRPC), using application rate limiting, authentication, and observability (Apigee, Cloud API Gateway), integrating applications using asynchronous or event-driven approaches (Eventarc, Pub/Sub), optimizing for cost and resource usage, understanding data replication to support zonal and regional failover models, using traffic splitting strategies (gradual rollouts, rollbacks, A/B testing) on Cloud Run or GKE, orchestrating application services with Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler. Designing secure applications including implementing data retention and organization policies (Cloud Storage Object Lifecycle Management, Cloud Storage use and lock retention policies), using security mechanisms that identify vulnerabilities and protect services and resources (Identity-Aware Proxy, 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 (Secret Manager, Cloud Key Management Service, Workload Identity Federation), authenticating to Google Cloud services (Application Default Credentials, JSON Web Token, OAuth 2.0, Cloud SQL Auth Proxy, AlloyDB Auth Proxy), managing and authenticating end-user accounts (Identity Platform), securing cloud resources using Identity and Access Management (IAM) roles for service accounts, securing service-to-service communications (Cloud Service Mesh, Kubernetes Network Policies), running services with least privileged access, securing application artifacts using Binary Authorization. Storing and accessing data including selecting the appropriate storage system based on volume of data and performance requirements, designing appropriate schemas for structured databases (AlloyDB, Spanner) and unstructured databases (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 including 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 including using Cloud Build and Artifact Registry to build and store containers from source code, configuring provenance in Cloud Build (Binary Authorization). Testing including writing unit tests with the help of Gemini Code Assist, executing automated integration tests in Cloud Build.

Deploying applications to Cloud Run including deploying applications from source code, invoking Cloud Run services using triggers (Eventarc, Pub/Sub), configuring event receivers (Eventarc, Pub/Sub), exposing and securing APIs in applications (Apigee), deploying a new API version in Cloud Endpoints considering backward compatibility. Deploying containers to GKE including 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 including managing connections to various Google Cloud datastores (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 including enabling Google Cloud services, making API calls by using supported options (Cloud Client Libraries, REST API, gRPC, API Explorer) taking into consideration batching requests, restricting return data, paginating results, caching results, handling errors (exponential backoff), using service accounts to make Cloud API calls. Troubleshooting and observability including 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.