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You have an Azure Synapse Analytics dedicated SQL pool.
You need to create a table named FactInternetSales that will be a large fact table in a dimensional model. FactInternetSales will contain 100 million rows and two columns named SalesAmount and OrderQuantity. Queries executed on FactInternetSales will aggregate the values in SalesAmount and OrderQuantity from the last year for a specific product. The solution must minimize the data size and query execution time.
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You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. Table1 contains the following:
✑ One billion rows
✑ A clustered columnstore index
✑ A hash-distributed column named Product Key
✑ A column named Sales Date that is of the date data type and cannot be null
Thirty million rows will be added to Table1 each month.
You need to partition Table1 based on the Sales Date column. The solution must optimize query performance and data loading.
How often should you create a partition?

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