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Refer to the exhibits.
An app is built to analyze salesperson performance by department. Departments are unique within the Departments table, but Salespeople often move between departments. A strict business rule states that a salesperson must be associated with ONLY one department at all times.
The data architect creates a summary of department performance and notices the values are incorrect. The total sales KPI shows the correct result.
How should the data architect modify the data model to correct this issue?
A table is generated resulting from the following script
When the data architect selects a date, some, but NOT all, orders for that date are shown How should the data architect modify the script to show all orders for the selected date?
A)
B)
C)
D)
A data architect needs to write the expression for a measure on a KPI to show the sales person with the highest sales The sort order of the values of the fields is unknown When two or more sales people have sold the same amount, the expression should return all of those sales people
Which expression should the data architect use?
The Marketing department is using some similar KPIs in different apps that need to be modified frequently according to the business needs. The KPIs are created using master items with the same expression.
Which method should the data architect use to manage the modifications in all apps?
A variable repository is a central location where all of the master items used in the apps can be stored and managed. By creating a variable repository and loading the master items into each app using INCLUDE statements, the data architect can make modifications to the master items in one place, and the changes will be automatically propagated to all of the apps that use the master items.
This method allows for easy management of the master items and eliminates the need to make changes to each app individually, which can be time-consuming and prone to errors.
A data architect plans to build an app that contains geographically diverse data that must be specific to user run-time selections. The source contains transactional dat
a. The app must have minimal impact on already limited server resources.
Which approach should the data architect use?
Using QVDs is the best approach for this scenario, as it allows the data to be stored in a highly compressed format, which will have minimal impact on server resources. Additionally, QVDs can be loaded quickly, which allows for faster access to the data based on user run-time selections. The other options, Loop and Reduce, In-memory, and ODAG, are not valid strategies for this scenario.
