DP-203 · Question #167
You are designing a data mart for the human resources (HR) department at your company. The data mart will contain employee information and employee transactions. From a source system, you have a…
The correct answer is C. a dimension table for Employee E. a fact table for Transaction. C: Dimension tables contain attribute data that might change but usually changes infrequently. For example, a customer's name and address are stored in a dimension table and updated only when the customer's profile changes. To minimize the size of a large fact table, the…
Question
You are designing a data mart for the human resources (HR) department at your company. The data mart will contain employee information and employee transactions. From a source system, you have a flat extract that has the following fields:
EmployeeID FirstName LastName Recipient GrossAmount TransactionID GovernmentID NetAmountPaid TransactionDate You need to design a star schema data model in an Azure Synapse Analytics dedicated SQL pool for the data mart. Which two tables should you create? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Options
- Aa dimension table for Transaction
- Ba dimension table for EmployeeTransaction
- Ca dimension table for Employee
- Da fact table for Employee
- Ea fact table for Transaction
How the community answered
(69 responses)- A4% (3)
- B3% (2)
- C84% (58)
- D9% (6)
Explanation
C: Dimension tables contain attribute data that might change but usually changes infrequently. For example, a customer's name and address are stored in a dimension table and updated only when the customer's profile changes. To minimize the size of a large fact table, the customer's name and address don't need to be in every row of a fact table. Instead, the fact table and the dimension table can share a customer ID. A query can join the two tables to associate a customer's profile and transactions. E: Fact tables contain quantitative data that are commonly generated in a transactional system, and then loaded into the dedicated SQL pool. For example, a retail business generates sales transactions every day, and then loads the data into a dedicated SQL pool fact table for analysis. https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data- warehouse-tables-overview
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