You are deploying a Python application to Cloud Run using Cloud Build. The Cloud Build pipeline is shown below:
You want to optimize deployment times and avoid unnecessary steps What should you do?
You are a lead developer working on a new retail system that runs on Cloud Run and Firestore. A web UI requirement is for the user to be able to browse through alt products. A few months after go-live, you notice that Cloud Run instances are terminated with HTTP 500: Container instances are exceeding memory limits errors during busy times
This error coincides with spikes in the number of Firestore queries
You need to prevent Cloud Run from crashing and decrease the number of Firestore queries. You want to use a solution that optimizes system performance What should you do?
You are supporting a business-critical application in production deployed on Cloud Run. The application is reporting HTTP 500 errors that are affecting the usability of the application. You want to be alerted when the number of errors exceeds 15% of the requests within a specific time window. What should you do?
You are developing a new web application using Cloud Run and committing code to Cloud Source Repositories. You want to deploy new code in the most efficient way possible. You have already created a Cloud Build YAML file that builds a container and runs the following command: gcloud run deploy. What should you do next?
Cloud Build uses build triggers to enable CI/CD automation. You can configure triggers to listen for incoming events, such as when a new commit is pushed to a repository or when a pull request is initiated, and then automatically execute a build when new events come in. You can also configure triggers to build code on any changes to your source repository or only on changes that match certain criteria.