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You have a Microsoft Azure SQL data warehouse that has a fact table named FactOrder. FactOrder contains three columns named CustomerID, OrderID, and OrderDateKey. FactOrder is hash distributed on CustomerID. OrderID is the unique identifier for FactOrder. FactOrder contains 3 million rows. Orders are distributed evenly among different customers from a table named dimCustomers that contains 2 million rows. You often run queries that join FactOrder and dimCustomers by selecting and grouping by the OrderDateKey column. You add 7 million rows to FactOrder. Most of the new records have a more recent OrderDateKey value than the previous records. You need to reduce the execution time of queries that group on OrderDateKey and that join dimCustomers and FactOrder. What should you do?
Correct Answer: C
Explanation/Reference: Updating statistics One best practice is to update statistics on date columns each day as new dates are added. Each time new rows are loaded into the data warehouse, new load dates or transaction dates are added. These change the data distribution and make the statistics out of date. References: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables- statistics
