A silver transformation in Microsoft Fabric reads a customer feed where the Region column is sometimes null because the upstream system omits it for older records. Downstream reports group revenue by Region and must show these records under a single bucket labelled Unknown rather than dropping them or leaving blanks that break the grouping. Which handling of the missing Region values best meets the requirement?
- AFilter out every row whose Region is null in the silver layer so the downstream grouping only ever sees populated Region values.
- BLeave the Region nulls in place and rely on the reporting layer to coalesce nulls into their own group at query time on each run.
- CReplace null Region values with the literal string Unknown during the silver transformation so every record carries a non-null grouping value. Correct
- DImpute the missing Region by copying the most frequent Region in the feed into each null so the records join a real, populated region group.
Why A is wrong: Dropping null-region rows produces clean grouping keys but discards valid revenue records, so totals understate reality; the requirement explicitly says these records must still appear, just under an Unknown bucket.
Why B is wrong: Deferring the fix to the report works only if every consumer remembers to coalesce, and many grouping engines treat nulls inconsistently; standardising the value in silver is more reliable than trusting each downstream query.
Why C is correct: Substituting a sentinel value such as Unknown for nulls keeps every record in the dataset and gives the grouping a single explicit bucket, which is exactly the behaviour the report requires for the missing-region rows.
Why D is wrong: Filling nulls with the most common region invents a location the record never had and inflates that region's revenue with misattributed rows; the requirement wants a distinct Unknown bucket, not a guessed real region.