Google Professional-Cloud-DevOps-Engineer Exam With Confidence Using Practice Dumps

The best option for determining whether the increase in availability is worth the investment for a single year of usage is to calculate the value of improved availability to be $900, and determine that the increase in availability is not worth the investment. To calculate the value of improved availability, we can use the following formula:

Value of improved availability = Revenue * (New availability - Current availability)

Plugging in the given numbers, we get:

Value of improved availability = $1,000,000 * (0.9999 - 0.999) = $900

Since the value of improved availability is less than the investment of $2,000, we can conclude that the increase in availability is not worth the investment.

To allow manual QA approval, you need separate pipeline stages (e.g., staging → production) and manual promotion between them.

"Cloud Deploy supports multi-stage pipelines where you can manually approve releases before promotion."

— Cloud Deploy Promotion

"Use a standard strategy when there are no automated tests or SLOs to support rollback decisions."

— Deployment Strategies

Canary is best used when automated health checks and metrics exist, which is not the case here.

The best option for deploying a new version of your containerized application to production on GKE and ensuring that the application does not have performance problems after deployment is to deploy the application through a continuous delivery pipeline by using canary deployments, use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics. A canary deployment is a deployment strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test the new version in the production environment with real traffic and load, and gradually increase the traffic as the metrics indicate. You can use Cloud Monitoring to collect and analyze metrics from your application and GKE cluster, such as latency, error rate, CPU utilization, and memory usage. You can also use Cloud Monitoring to set up alerts and dashboards to track the performance of your application.