A Data Analyst has a very large table with columns that contain country and city...

When working with very large tables, calculating the exact number of unique combinations of columns (cardinality) using COUNT(DISTINCT ...) is a resource-intensive operation. It requires the Snowflake query engine to keep an exhaustive list of every unique pair encountered in memory, which can lead to high credit consumption and performance bottlenecks.

To provide a "very quick estimate," Snowflake utilizes the HyperLogLog (HLL) algorithm. The function HLL(column1, column2, ...) returns an HLL state (a binary representation) that can be used to estimate the number of distinct values with a high degree of accuracy and minimal computational overhead. This is part of Snowflake's suite of approximate aggregation functions, which are essential for Data Analysis on massive datasets where a 1% margin of error is acceptable in exchange for significantly faster results.

Evaluating the Options:

Option A is syntactically incorrect; DISTINCT cannot be used in that position with COUNT.

Option C is a valid query but will be significantly slower and more expensive than an HLL estimate on a "very large table".

Option D simply counts the total number of non-null rows, which does not represent the "number of different values" (cardinality).

Option B is the 100% correct answer. It specifically addresses the requirement for a "quick estimate" using the industry-standard probabilistic counting method built into Snowflake.