How are results generated each time a Databricks SQL dashboard is updated using the query SELECT COUNT (*) FROM table?

The correct choice highlights the way Delta Lake manages data and optimizations in Databricks. When executing the query SELECT COUNT(*) FROM table, the total count of records can actually be derived from the Delta transaction logs. Delta Lake maintains a transaction log that keeps track of all changes made to the data, including updates, inserts, and deletes.

Using the transaction logs, Delta Lake can efficiently answer queries about the state of the data without needing to scan all the underlying data files directly. This method not only provides performance benefits due to reduced I/O but also ensures accuracy by reflecting the latest state of the table.

In this case, reliance on transaction logs avoids the overhead associated with full data scans while still providing correct and up-to-date results for operations like counting rows in a dataset.

Other approaches mentioned are less efficient or not aligned with how Delta Lake operates. For example, scanning all data files would lead to higher latency and unnecessary processing. Returning results from cached queries might be relevant, but it doesn't take advantage of the Delta transaction logs specifically. Finally, while parquet file metadata can provide some aggregate information, it cannot accurately account for all changes over time, especially when dealing with updates or deletions. Thus, transaction logs represent the most efficient and reliable means