You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also want to reliably deploy new versions of the application. Which set of steps should you take?

The correct option leverages GKE for container orchestration, which is ideal for managing microservices, maximizing machine utilization through efficient resource scheduling, and providing reliable deployments. Building a Docker image with dependencies ensures consistent environments. Using a staging namespace for testing before promoting to production is a robust deployment strategy. Tagging images with version numbers allows for rollbacks and clear identification. The first incorrect option uses f1-micro machines, which are too small for a production application, and relies on startup scripts for deployments, which is error-prone and not scalable. The second incorrect option uses Compute Engine images, which are less flexible and efficient for application deployments compared to containers, and rebuilding images for every update is slow. The fourth incorrect option uses n1-standard-4 machines, which are oversized for the stated requirements (0.1 CPU, 128MB RAM), leading to wasted resources. Deploying directly to the default namespace without staging is risky, and using the 'latest' tag can lead to unpredictable deployments.