A data quality reviewer loads a cuDF DataFrame of customer records and wants a single number per column showing how many entries are missing, so she can decide which columns need imputation. She plans to chain a null-detection step with a per-column total. Which combination of cuDF operations gives the count of missing values in each column?
- AApply the duplicated operation to mark repeated rows, then total those marks per column to count the missing entries.
- BApply the isnull operation to produce a boolean mask, then total the True values down each column with a column-wise sum. Correct
- CApply the unique operation to each column, then total the distinct values to count the missing entries.
- DApply the describe operation, then read the standard deviation row as the count of missing entries per column.
Why A is wrong: The duplicated operation flags repeated rows, not absent values, so totalling its marks reports duplication rather than missingness and answers a different question.
Why B is correct: Isnull returns a boolean DataFrame where True marks each missing entry, and summing those booleans down each column counts the missing values per column, which is the requested per-column missingness tally.
Why C is wrong: Unique lists the distinct values in a column, so totalling them measures cardinality; nulls are not counted as a value to sum, and this does not yield a missing-value total.
Why D is wrong: Describe reports summary statistics such as count, mean and standard deviation; standard deviation measures spread, not missingness, so reading it as a null count is incorrect.