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38. Frage
A startup company is about have its Initial Public Offering (IPO) on the New York Stock Exchange.
This startup company has used Qlik Sense for many years for data-based decision making for Sales and Marketing efforts, as well as for input into Financial Reporting. The startup's Qlik Sense applications use variables that have different values at different points in time.
Due to the increased rigor required in record keeping for public companies, these variables must be clearly recorded in the script reload logs of the Qlik Sense applications. These logs are refreshed daily.
The data architect wants to have the variables names, with their current values,writteninto the script reload logs. Which script statement should the data architect use?
Antwort: D
Begründung:
In the scenario where the startup company is preparing for an IPO, there is an increased need for meticulous record-keeping, including the recording of variable values used in Qlik Sense applications. The TRACE statement is the most suitable option for logging variable values during script execution.
* TRACE: This statement writes custom messages, including variable values, to the script execution log.
By using TRACE, you can ensure that every reload log contains the names and current values of all relevant variables, providing the necessary transparency and traceability.
For example, the script could include:
TRACE $(VariableName);
This command will output the variable's value in the script log, ensuring it is recorded for audit purposes.
39. Frage
Exhibit.
Refer to the exhibits.
The Orders table contains a list of orders and associated details. A data architect needs to replace the SupplierlD with the SupplierName using the second table as the source.
The output must be a single table.
Which script should the data architect use?
Antwort: A
Begründung:
In this scenario, the data architect needs to replace the SupplierID in the Orders table with the corresponding SupplierName from the Suppliers table, and the desired output should be a single table that includes all the order details along with the SupplierName instead of the SupplierID.
Analyzing the Options:
* Option A:
* Uses a MAPPING LOAD followed by an APPLYMAP to replace SupplierID with SupplierName in the Orders table. However, the table is dropped afterward, which means it won't produce the required output.
* The MAPPING LOAD approach is generally used to map values but is not necessary in this context as we are combining data from two tables directly.
* Option B:
* This option attempts to LEFT JOIN the Products table with the Suppliers table, but it does not directly address replacing SupplierID with SupplierName in the Orders table.
* Additionally, it does not remove the SupplierID after the join, which is essential for the correct output.
* Option C:
* This option uses a LEFT JOIN with the DISTINCT keyword on the SupplierID field to avoid duplicates. The SupplierName is correctly joined to the Orders table, replacing the SupplierID.
* This approach is the most appropriate because it results in a single table containing all order details with the SupplierName instead of the SupplierID.
* Option D:
* Similar to Option A, but it also introduces an unnecessary renaming step with MAPPING LOAD.
It's redundant and does not improve the solution over Option C.
Correct Script Choice:
Option Cis the correct script because:
* It ensures that SupplierName replaces SupplierID in the Orders table using a LEFT JOIN.
* The DISTINCT keyword is applied to the SupplierID field to prevent duplicate rows during the join.
* The result is a single table containing the required information with SupplierName in place of SupplierID.
References:
* Qlik Sense Join Operations: Using the correct JOIN type and ensuring proper deduplication (with DISTINCT if necessary) is key to merging tables in Qlik Sense.
40. Frage
Exhibit.
Refer to the exhibit.
A data architect is loading two tables into a data model from a SQL database. These tables are related on key fields CustomerlD and Customer Key.
Which script should the data architect use?
Antwort: B
Begründung:
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.
41. Frage 
Refer to the exhibit
A large transport company (Company A) acquires a smaller rival (Company B).
Company A has been using Qlik Sense tor 6 years to track revenue per ship journey. Ship journeys with no revenue (such as journeys to shipyards for repair) always show revenue of $0.
Company A wants to combine its data set with the data set of the acquired Company B. Company B's ship journey data shows $0 revenue in one of the following ways:
* A NULL value
* A value with one or more blank spaces (ASCII char code 32)
The data architect wants to conform the Company B data to the Company A standard, specifically regarding the use of an explicit $0 for journeys without revenue. Which script line should the data architect use?
Antwort: A
Begründung:
In this scenario, the data architect needs to conform the revenue data from Company B to match the data standard of Company A, where $0 is explicitly used to represent journeys without revenue.
Explanation of the Correct Script:
* Option A:money(replace(Revenue, chr(32), 0)) AS [Revenue Conformed]
* replace(Revenue, chr(32), 0):This part of the expression replaces any spaces (ASCII character code 32) in the Revenue field with 0.
* money(...):This function formats the resulting value as currency. Since Company B may have either null values or spaces where 0 should be, this script ensures that any blanks are replaced with 0 and then formatted as currency.
Why Option A is Correct:
* Handling Spaces:The replace() function is effective in replacing spaces with 0, conforming to Company A's standard of using $0 for non-revenue journeys.
* Handling NULL Values:The money() function is used to ensure the final output is formatted as currency. However, it's important to note that NULL values are not directly handled by the replace() function, which is why it is applied before money() to deal with spaces.
42. Frage
A data architect executes the following script:
What will be the result of Table.A?
Antwort: D
Begründung:
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
43. Frage
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