You are using Cloud SQL as the database backend for a large CRM deployment. You want to scale as usage increases and ensure that you don't run out of storage, maintain 75% CPU usage cores, and keep replication lag below 60 seconds. What are the correct steps to meet your requirements?

The correct answer addresses all three requirements effectively. Enabling automatic storage increase directly prevents running out of storage. Creating a Stackdriver (now Cloud Monitoring) alert for CPU usage and then changing the instance type (scaling up) is a reactive and efficient way to maintain CPU usage below 75%. Sharding the database is a robust solution for reducing replication lag in a large deployment by distributing the write load and data across multiple instances, which improves overall performance and reduces the burden on a single replica. Incorrect options: Changing instance type to a fixed 32-core machine type without monitoring doesn't guarantee CPU usage below 75% as load can still increase, and deploying Memcache helps with read load but doesn't directly address replication lag caused by write volume. Manually increasing storage after an alert is reactive but less efficient than automatic increase, and deploying Memcached helps CPU but changing instance type alone might not resolve replication lag if the issue is high write volume on a single master. Similar to the previous, manual storage increase is less optimal, and while Memcached helps CPU, changing instance type to reduce replication lag might not be sufficient if the underlying problem is the volume of writes on a single master.