Qlik QSDA2024 Dumps

In the scenario, two tables (OrderDetails and Customers) are being loaded into the Qlik Sense data model, and these tables are related via the fields CustomerID and CustomerKey. The goal is to ensure that the relationship between these two tables is correctly established in Qlik Sense without creating synthetic keys or data inconsistencies.

Option A:Renaming CustomerKey to CustomerID in the OrderDetails table ensures that the fields will have the same name across both tables, which is necessary to create the relationship. However, renaming is done using AS, which might create an issue if the fields in the original data source have a different meaning.

Option B and C:These options use AUTONUMBER to convert the CustomerKey and CustomerID to unique numeric values. However, using AUTONUMBER for both fields without ensuring they are aligned correctly might lead to incorrect associations since AUTONUMBER generates unique values based on the order of data loading, and these might not match across tables.

Option D:This approach loads the tables with their original field names and then uses the RENAME FIELD statement to align the field names (CustomerKey to CustomerID). This ensures that the key fields are correctly aligned across both tables, maintaining their relationship without introducing synthetic keys or mismatches.

In the script provided, there are two tables being loaded inline: Table_A and Table_B. The script uses the Join function to combine Table_B with Table_A based on the common field Field_1. Here’s how the join operation works:

Table_Ainitially contains three records with Field_1 values of 01, 01, and 02.

Table_Bcontains two records with Field_1 values of 01 and 03.

When Join(Table_A) is executed, Qlik Sense will perform an inner join by default, meaning it will join rows from Table_B to Table_A where Field_1 matches in both tables. The result is:

For Field_1 = 01, there are two matches in Table_A and one match in Table_B. This results in two records in the joined table where Field_4 and Field_5 values from Table_B are repeated for each match in Table_A.

For Field_1 = 02, there is no corresponding Field_1 = 02 in Table_B, so the Field_4 and Field_5 values for this record will be null.

For Field_1 = 03, there is no corresponding Field_1 = 03 in Table_A, so the record from Table_B with Field_1 = 03 is not included in the final joined table.

Thus, the correct output will look like this:

Field_1 = 01, Field_2 = AB, Field_3 = 10, Field_4 = 30%, Field_5 = 500

Field_1 = 01, Field_2 = AC, Field_3 = 50, Field_4 = 30%, Field_5 = 500

Field_1 = 02, Field_2 = AD, Field_3 = 75, Field_4 = null, Field_5 = null

In the provided scenario, the sales territories were realigned during the year, and it is necessary to track performance based on the date of the sale and the salesperson’s assignment during that period. The IntervalMatch function is the best approach to create a time-based relationship between the sales transactions and the sales territory assignments.

IntervalMatch: This function is used to match discrete values (e.g., transaction dates) with intervals (e.g., start and end dates for sales territory assignments). By matching the transaction dates with the intervals in the HR table, you can accurately determine which territory and manager were in effect at the time of each sale.

Using IntervalMatch, you can generate point-in-time data that accurately reflects the dynamic nature of sales territory assignments, allowing both sales reps and regional managers to track performance over time.