Which Dataflows Editor option shows distinct values for a column and helps identify non-duplicate entries across customers?

Study for the Fabric Analytics Engineer Associate Test. Use flashcards and multiple choice questions, each with hints and explanations. Get ready for your exam!

Multiple Choice

Which Dataflows Editor option shows distinct values for a column and helps identify non-duplicate entries across customers?

Explanation:
Profiling a column to see how its values are distributed helps you spot duplicates. The column distribution view that shows distinct values lists each unique value in the column and shows how many times it occurs. By examining that frequency, you can identify entries that appear only once, i.e., non-duplicate entries across customers, in a single view. That makes it the best fit for identifying non-duplicates because you get both the set of distinct values and their counts together. The other options miss the mark for this goal: validating values checks whether values meet allowed rules rather than how often they appear; showing total values counts documents how many rows there are but not which values are duplicates; and a view labeled as unique values would focus on items that occur just once but doesn’t always present the full distribution of all distinct values with their frequencies.

Profiling a column to see how its values are distributed helps you spot duplicates. The column distribution view that shows distinct values lists each unique value in the column and shows how many times it occurs. By examining that frequency, you can identify entries that appear only once, i.e., non-duplicate entries across customers, in a single view. That makes it the best fit for identifying non-duplicates because you get both the set of distinct values and their counts together.

The other options miss the mark for this goal: validating values checks whether values meet allowed rules rather than how often they appear; showing total values counts documents how many rows there are but not which values are duplicates; and a view labeled as unique values would focus on items that occur just once but doesn’t always present the full distribution of all distinct values with their frequencies.

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