You have an Azure SQL database that contains a customer dimension table. The table contains two columns named CustomerID and CustomerCompositeKey. You have a Fabric workspace that contains a Dataflow Gen2 query that connects to the database. You need to use Dataflows Query Editor to identify which of the two columns contains non-duplicate values per customer. Which option should you use?

You have an Azure SQL database that contains a customer dimension table. The table contains two columns named CustomerID and CustomerCompositeKey. You have a Fabric workspace that contains a Dataflow Gen2 query that connects to the database. You need to use Dataflows Query Editor to identify which of the two columns contains non-duplicate values per customer. Which option should you use?

• A. Column distribution - distinct values
• B. Column distribution - max values
• C. Column distribution - missing values
• D. Column distribution - duplicates

Answer: (A)

Think about how many times each value appears in a column. In Dataflows you can view a column’s distribution and the number of distinct values. If a column has as many distinct values as there are rows, every row has a unique value in that column, meaning there are no duplicates for that column. That’s exactly what you want when identifying a non-duplicate value per customer. The distinct values view lets you make that direct comparison: you can see the total row count and the count of distinct values and determine if a column is unique for each customer. The other options don’t give you a clean, direct check for overall uniqueness—duplicates shows only whether duplicates exist, missing values doesn’t address duplicates, and max values isn’t relevant to determining uniqueness.

Think about how many times each value appears in a column. In Dataflows you can view a column’s distribution and the number of distinct values. If a column has as many distinct values as there are rows, every row has a unique value in that column, meaning there are no duplicates for that column. That’s exactly what you want when identifying a non-duplicate value per customer. The distinct values view lets you make that direct comparison: you can see the total row count and the count of distinct values and determine if a column is unique for each customer. The other options don’t give you a clean, direct check for overall uniqueness—duplicates shows only whether duplicates exist, missing values doesn’t address duplicates, and max values isn’t relevant to determining uniqueness.