This answer is correct because it meets the requirements of having an optimal and highly available environment with automatic failover. According to the Google Cloud documentation1, a regional Cloud SQL instance is an instance that has a primary server in one zone and a standby server in another zone within the same region. The primary and standby servers are kept in sync using synchronous replication, which ensures zero data loss and minimal downtime in case of a zonal outage or an instance failure. If the primary server becomes unavailable, Cloud SQL automatically fails over to the standby server, which becomes the new primary server1.

https://cloud.google.com/blog/products/identity-security/meet-data-residency-requirements-with-google-cloud

https://cloud.google.com/spanner/docs/schema-design D because high cardinality means you have more unique values in the collumn. That's a good thing for a hot-spotting issue. E because Spanner specifically has this feature to reduce hot spotting. Basically, it generates unique values https://cloud.google.com/spanner/docs/schema-design#bit_reverse_primary_key

D. Promote high-cardinality attributes in multi-attribute primary keys.

This is a correct answer because promoting high-cardinality attributes in multi-attribute primary keys can help avoid hotspots in Cloud Spanner. High-cardinality attributes are those that have many distinct values, such as UUIDs, email addresses, or timestamps1. By placing high-cardinality attributes first in the primary key, you can ensure that the rows are distributed more evenly across the key space, and avoid having too many requests sent to the same server2.

E. Use bit-reverse sequential value as the primary key.

This is a correct answer because using bit-reverse sequential value as the primary key can help avoid hotspots in Cloud Spanner. Bit-reverse sequential value is a technique that reverses the bits of a monotonically increasing value, such as a timestamp or an auto-incrementing ID1. By reversing the bits, you can create a pseudo-random value that spreads the writes across the key space, and avoid having all the inserts occurring at the end of the table2.

Financial data, such as transaction histories, stock prices, and currency exchange rates.

https://cloud.google.com/bigtable/docs/overview#what-its-good-for

With SSD:

Reads - up to 10,000 rows per second

Writes - up to 10,000 rows per second

Scans - up to 220 MB/s

https://cloud.google.com/bigtable/docs/performance#typical-workloads

Configure a maintenance window during a period when no users will be on the system. Control the order of update by setting non-production instances to earlier and production instances to later.

https://cloud.google.com/bigtable/docs/performance#troubleshooting

You could migrate to Spanner leveraging the PostgreSQL dialect, but costs need to be minimized so that wouldn’t be the cheapest option. Especially since the load doesn’t justify Spanner. Again, you could migrate like-for-like to a GCE VM, but that defeats minimizing the migration effort. The cheapest and easiest way to migrate would be Database Migration Service to Cloud SQL for PostgreSQL.