In a "Dataflows Gen2" query, a Supplier column holds the same supplier name with inconsistent leading and trailing spaces and stray non-printing control characters, so values that should match are treated as distinct and a later Remove Duplicates leaves several near-identical rows. The engineer must make matching suppliers collapse correctly. Which TWO transformations should be applied to the Supplier column before the deduplication step? Select TWO.
- AApply the Trim transformation to the Supplier column so the leading and trailing spaces are stripped and otherwise equal values become byte-for-byte identical. Correct
- BApply Capitalise Each Word to the Supplier column so the casing is standardised across every row and the supplier names then line up consistently for matching.
- CApply the Clean transformation to the Supplier column so the stray non-printing control characters are removed and the remaining text compares as equal across rows. Correct
- DApply Replace Values on the Supplier column to swap one specific malformed name for its correct spelling so that the affected rows then match the clean entries.
Why A is correct: Trim removes the surrounding whitespace that makes the same supplier compare as different, which is one of the two cleansing steps needed before Remove Duplicates can collapse the rows.
Why B is wrong: Changing the casing is tempting as a normaliser, but the stem describes whitespace and control characters, not casing, so this step alters the data without fixing the stated mismatch.
Why C is correct: Clean strips the non-printing control characters called out in the stem, which is the second cleansing step required so that otherwise identical supplier values match during deduplication.
Why D is wrong: Replace Values fixes a single named value but cannot address the general whitespace and control-character problem across every supplier, so most near-identical rows would still fail to collapse.