Answer: `SELECT count_if(member_id IS NULL) FROM my_table;`

## Explanation Let's analyze each option: **Option A**: `SELECT count(member_id) FROM my_table;` - This returns the count of **non-null** values in the member_id column, not null values. - The `COUNT()` function in SQL excludes NULL values by default. **Option B**: `SELECT count(member id)-count null(member_id) FROM my_table;` - This has syntax errors: - `member id` should be `member_id` (missing underscore) - `count null()` is not a valid SQL function - Even if corrected, this would try to subtract an invalid function call **Option C**: `SELECT count_if(member_id IS NULL) FROM my_table;` ✓ **CORRECT** - `count_if()` is a Spark SQL function that counts rows where the condition is true. - `member_id IS NULL` correctly identifies null values. - This will return the exact count of null values in the member_id column. **Option D**: `SELECT null member_jd) FROM my_table;` - This has multiple syntax errors: - `member_jd` appears to be a typo (should be `member_id`) - Missing opening parenthesis - `null` is not a function - This is not valid SQL syntax **Option E**: `SELECT count null(member id) FROM my_table;` - This has syntax errors: - `member id` should be `member_id` (missing underscore) - `count null()` is not a valid SQL function - Should be `count_if()` or use `COUNT(*)` with a `WHERE` clause ## Alternative Correct Approaches In Spark SQL, you could also use: 1. `SELECT COUNT(*) FROM my_table WHERE member_id IS NULL;` 2. `SELECT SUM(CASE WHEN member_id IS NULL THEN 1 ELSE 0 END) FROM my_table;` ## Key Learning Points - `COUNT(column_name)` excludes NULL values - `COUNT(*)` counts all rows including NULLs - `count_if(condition)` counts rows where the condition evaluates to TRUE - `IS NULL` is the correct operator to check for null values in SQL

Author: Keng Suppaseth