Understanding how Snowflake aggregate functions like MIN() and MAX() handle numerical data and NULL values is fundamental for accurate Data Analysis. In this scenario, we have a table with five records distributed across two departments.
The MIN() function returns the smallest non-null value in the specified column across all rows in the group (or the entire table, if no GROUP BY is present). Looking at the salary column in the employees table, the values are: 10000, 9000, 8000, 15000, and NULL. The NULL value is ignored by the calculation. Among the remaining numerical values, 8000 is the smallest. Therefore, MIN_VAL will be 8000.
The MAX() function operates similarly, returning the largest non-null value from the set. Comparing the same list of numerical values—10000, 9000, 8000, and 15000—the largest value is clearly 15000. Consequently, MAX_VAL will be 15000.
Evaluating the Options based on the exhibit (image_8ca0df.png):
Option A incorrectly identifies the minimum and maximum based on the employee_id column (where 2000 is max and 900 is min), rather than the requested salary column.
Option B is incorrect as it seems to mix values from different columns or specific rows.
Option C is incorrect because it mistakenly suggests that MIN() returns NULL if a NULL value is present in the column. In Snowflake, standard aggregate functions (except for specialized ones like ARRAY_AGG or certain window functions with specific clauses) skip NULL values entirely.
Option D is the 100% correct output. It displays MIN_VAL as 8000 and MAX_VAL as 15000, which accurately reflects the mathematical minimum and maximum of the non-null entries in the salary column.
This behavior is consistent across all relational databases adhering to ANSI SQL standards, which Snowflake follows for these core aggregate operations.
