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A data architect is developing an app that will generate QVDs for multiple business analysts. The field naming conventions on the source data are NOT business friendly. For every table loaded, multiple fields will require a name change.
An Excel file is maintained centrally that lists all source data field names and the appropriate names as they should appear in the QVDs
Which strategy should the data architect use to meet these requirements?
The Rename function allows the data architect to rename fields from the source data to the names specified in the Excel file. The mapping load allows the data architect to load the mapping between the source field names and the business-friendly names from the Excel file. The other options, Create master items using business-friendly names, Use the Alias function and a mapping load, and Load in the Excel file as a data island and use the Peek function, are not valid strategies for meeting these requirements.
The Section Access security table for an app is shown. User ABC\PPP opens a Qlik Sense app with a table using the field called LEVEL on one of the table columns.
What is the result?
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.
Refer to the exhibit.
Refer to the exhibits.
While using an app, the users report that some Sales Reps do NOT have personal details, like Division or Address Number A data architect has been called in to investigate.
The data architect uses the data model viewer to determine the relationship between the SalesRepMaster and CustomerMaster tables.
What is the cause of the issue?
When using the data model viewer to investigate the relationship between the SalesRepMaster and CustomerMaster tables, the data architect would look at the density of the relationship. Density is a measure of how well the key fields of a table match the key fields of another table. A density of 100% means that all key fields in one table have a match in the other table.
When the density is 100% but the total distinct and present distinct values for the key fields of the related tables do not match, it means that some of the key fields in one table do not have a match in the other table, this is the cause of the issue.
A data architect wants to combine data on present and historic sales performance. The historic data is stored in a de-normalized archive, and the present data is maintained in a database. The output must be contained in a single table.
Which script should the data architect use?
A)
B)
C)
D)
