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Cloud & infrastructure · #gcp
Learn how to create a Cloud Scheduler job that securely triggers a Cloud Function, configure the required IAM roles, view logs, and understand cost and retry behavior.
Cloud Run's request concurrency setting trades cost against latency. Learn when to set it high, low, or 1, with a worked example and a load-test verification plan.
Enable Cloud SQL point-in-time recovery correctly (backups plus transaction logs), clone an instance to a UTC timestamp, verify the clone, and run a drill that proves the window is real.
Learn how to enable Google Cloud Storage Object Versioning and pair it with a lifecycle rule to automatically delete old non‑current versions, reducing storage costs while keeping recent data accessible.
Recovery needs to recreate the working service and its required data after a machine or process is lost. Which artifacts and state need protection, and how should the restore be checked?
The same project needs to behave consistently on developer machines, in CI and after deployment. Which versions, dependencies and configuration should be recorded?
A worked Cloud Run example references an enabled Secret Manager version as an environment variable. The deployer’s account can access the secret. The service uses a dedicated runtime service account, and new instances fail around startup. Which identity and permission should be checked?
Configuration must be available to the application without exposing credentials in source control, logs or browser code. What belongs in the runtime and which access controls matter?
Goal Lower the ongoing cost of running a low‑traffic workload on Google Cloud while keeping the service responsive to occasional requests. Context The Well‑Architected Framework’s cost‑optimization pillar recommends granular control of performance and cost parameters to maximize business value. It is unclear which Cloud Run‑specific setting (for example, CPU
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