Your team is writing a backend application to implement the business logic for an interactive voice response (IVR) system that will support a payroll application. The IVR system has the following technical characteristics:
* Each customer phone call is associated with a unique IVR session.
* The IVR system creates a separate persistent gRPC connection to the backend for each session.
* If the connection is interrupted, the IVR system establishes a new connection, causing a slight latency for that call.
You need to determine which compute environment should be used to deploy the backend application. Using current call data, you determine that:
* Call duration ranges from 1 to 30 minutes.
* Calls are typically made during business hours.
* There are significant spikes of calls around certain known dates (e.g., pay days), or when large payroll changes occur.
You want to minimize cost, effort, and operational overhead. Where should you deploy the backend application?
Your company has deployed a new API to a Compute Engine instance. During testing, the API is not behaving as expected. You want to monitor the application over 12 hours to diagnose the problem within the application code without redeploying the application. Which tool should you use?
Your company stores their source code in a Cloud Source Repositories repository. Your company wants to build and test their code on each source code commit to the repository and requires a solution that is managed and has minimal operations overhead.
Which method should they use?
You have a service running on Compute Engine virtual machine instances behind a global load balancer. You need to ensure that when the instance fails, it is recovered. What should you do?
You have an application running on Cloud Run that receives a large volume of traffic. You need to deploy a new version of the application. You want your deployment process to minimize the risk of downtime while following Google-recommended practices. What should you do?
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