Google Professional Cloud Developer Course Notes

Design the application from workload requirements: select the right compute and API pattern, isolate failure domains, secure identities and artifacts, and choose storage by data model, consistency, and access pattern.

• Choosing a familiar service without checking regionality, state, concurrency, or the required operational control.

• Underline the constraint that changes the answer: I/O-bound, non-concurrent, atomic, time-limited, administrator-resistant, or globally consistent.

Use a repeatable, authenticated development environment and a build pipeline that produces traceable artifacts. Gate promotion with deterministic unit and integration tests and review AI-assisted output like any other code.

• Using live production resources for local tests or confusing a workstation configuration with a deployed application.

• Choose the lightest environment that still provides the requested isolation, persistence, or administrative control.

Deploy from source or images with an explicit runtime contract, event and API integration, revision traffic control, Kubernetes health checks, and autoscaling settings that match the workload.

• Confusing a revision with a mutable server, or treating a trigger, service URL, and API gateway as the same integration layer.

• A Cloud Run configuration change creates a new revision; preserve the old revision when rollback evidence matters.

Integrate managed datastores and messaging with correct transaction and acknowledgement semantics, consume APIs with pagination and bounded retries, and troubleshoot by correlating metrics, logs, traces, and errors.

• Assuming a successful network call means durable processing, or acknowledging a message before the work is actually complete.

• Transactions protect read-dependent decisions; batched writes fit known writes that do not need a read phase.