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Refer to the exhibit.
A data architect needs to build a dashboard that displays the aggregated sates for each sales representative. All aggregations on the data must be performed in the script.
Which script should the data architect use to meet these requirements?
A)
B)
C)
D)
The goal is to display the aggregated sales for each sales representative, with all aggregations being performed in the script. Option C is the correct choice because it performs the aggregation correctly using a Group by clause, ensuring that the sum of sales for each employee is calculated within the script.
Data Load:
The Data table is loaded first from the Sales table. This includes the OrderID, OrderDate, CustomerID, EmployeeID, and Sales.
Next, the Emp table is loaded containing EmployeeID and EmployeeName.
Joining Data:
A Left Join is performed between the Data table and the Emp table on EmployeeID, enriching the data with EmployeeName.
Aggregation:
The Summary table is created by loading the EmployeeName and calculating the total sales using the sum([Sales]) function.
The Resident keyword indicates that the data is pulled from the existing tables in memory, specifically the Data table.
The Group by clause ensures that the aggregation is performed correctly for each EmployeeName, summarizing the total sales for each employee.
Key Qlik Sense Data Architect Reference:
Resident Load: This is a method to reuse data that is already loaded into the app's memory. By using a Resident load, you can create new tables or perform calculations like aggregation on the existing data.
Group by Clause: The Group by clause is essential when performing aggregations in the script. It groups the data by specified fields and performs the desired aggregation function (e.g., sum, count).
Left Join: Used to combine data from two tables. In this case, Left Join is used to enrich the sales data with employee names, ensuring that the sales data is associated correctly with the respective employee.
Conclusion: Option C is the most appropriate script for this task because it correctly performs the necessary joins and aggregations in the script. This ensures that the dashboard will display the correct aggregated sales per employee, meeting the data architect's requirements.