Pass the Qlik Sense Data Architect QSDA2024 Questions and answers with ExamsMirror

When retrieving data from a REST API, particularly when the dataset is large or the data is segmented across multiple pages (which is common in REST APIs), the REST Connector in Qlik Sense needs to be configured to handle pagination.

Pagination is the process of dividing the data retrieved from the API into pages that can be loaded sequentially or as required. Qlik Sense's REST Connector supports pagination by allowing the dataarchitect to set parameters that will sequentially retrieve each page of data, ensuring that the complete dataset is retrieved.

Key Steps:

REST Connector Setup: Configure the REST connector in Qlik Sense and specify the necessary API endpoint.

Pagination Mechanism: Use the built-in pagination mechanism to define how the connector should retrieve the subsequent pages (e.g., by using query parameters like page or offset).

In this scenario, the data architect needs to validate the script and specifically ensure that the vMaxDate variable is being correctly utilized in the LOAD statement. The goal is to stop the script execution at a point where the variable's value can be previewed.

Understanding the Options:

Option Aplaces the breakpoint just after the assignment of the variable vMaxDate in the Where clause but before any data is loaded.

Option B, C, and Drepresent placements of the breakpoint after the LOAD statement begins processing the Resident table, which means that the variable vMaxDate would have already been utilized.

Correct Breakpoint Placement:

Option Ais the correct choice because placing the breakpoint at this point allows you to preview the value of vMaxDate right before it is used in the Where clause. This placement ensures that the script execution halts before loading the data, allowing you to validate whether vMaxDate is correctly defined and whether it correctly filters the data based on the [Date] field.

If the breakpoint were placed after the LOAD statement (as in Options B, C, or D), the script would have already attempted to load the data, making it too late to inspect the variable's value before it's used.

References:

Qlik Sense Debugging Best Practices: When debugging, it is crucial to set breakpoints before the execution of a critical operation where the values of variables or fields are used to ensure that they hold the expected data.

In the scenario where the data model is built with a composite key (keyYearMonthCustNo) to resolve synthetic keys, the following outcomes occur:

Sales and Budget Data Integration:

The composite key ensures that each combination of Year, Month, and Customer is uniquely represented in the combined Sales and Budget data.

During data selection (e.g., when a specific month is selected), Qlik Sense will show all the customer names that have either Sales or Budget data associated with that month.

Resulting Data View:

For the selected month, customers with sales records will display their Sales data. However, if the corresponding Budget data is missing, the Budget column will contain null values.

Similarly, if a customer has a Budget but no Sales data for the selected month, the Sales column will show null values.

Validation Outcome:When the data architect selects a month, they will see the following:

Customer Names and Sales recordsfor the selected month, where the Sales column will have values and the Budget column may contain null or non-null values depending on the data availability.

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

The issue described relates to the incorrect display of month and year values on the x-axis of a chart. The source data has dates in the M/D/YYYY format, and a calculated field named Month-Year was created using this date format.

To correct the issue:

The correct approach is to use the MonthStart() function, which returns the first date of the month for the provided date. This ensures consistency in month-year representation.

The Date() function is then used to format the result of MonthStart() to the desired format of MMM-YYYY (e.g., Feb-2018).

Explanation of the Correct Expression:

MonthStart([Common Date]): This ensures that all dates within a month are treated as the first day of that month, which is critical for accurate monthly aggregation.

Date(..., 'MMM-YYYY'): This formats the result to show just the month and year in the correct format.

Using this expression ensures that the x-axis correctly displays the month-year values.

In the scenario, the data architect needs to load data from two different databases, and additional data is located in a folder containing QVDs, text files, and Excel files.

Minimum Number of Data Connections Required:

Database Connections:

Each database requires a separate data connection. Therefore, two data connections are needed for the two databases.

Folder Connection:

A single folder data connection can be used to access all the QVDs, text files, and Excel files in the specified folder. Qlik Sense allows you to create a folder connection that can access multiple file types within that folder.

Total Connections:

Two Database Connections: One for each database.

One Folder Connection: To access the QVDs, text files, and Excel files.

Therefore, the minimum number of data connections required istwo.

In this scenario, the data architect is using a GENERIC LOAD statement in the script to handle the data structure provided. A GENERIC LOAD is used in Qlik Sense when you have data in a key-value pair structure and you want to transform it into a more traditional table structure, where each attribute becomes a column.

Given the input data table with three columns (Object, Attribute, Value), and the attributes in the Attribute field being either color, diameter, length, or width, the GENERIC LOAD will create separate tables based on the combinations of Object and each Attribute.

Here's how the GENERIC LOAD works:

For each unique object(circle, rectangle, square), the GENERIC LOAD creates separate tables based on the distinct values of the Attribute field.

Each of these tableswill contain two fields: Object and the specific attribute (e.g., color, diameter, length, width).

Breakdown:

Table for circle:

Fields: Object, color, diameter

Table for rectangle:

Fields: Object, color, length, width

Table for square:

Fields: Object, color, length

Each distinct attribute (color, diameter, length, width) and object combination generates a separate table.

Final Count of Tables:

The script will create6 separate tables: one for each unique combination of Object and Attribute.

References:

Qlik Sense Documentation on Generic Load: Generic loads are used to pivot key-value pair data structures into multiple tables, where each key (in this case, the Attribute field values) forms a new column in its own table.

In this scenario, the key is composed of three components: Country, UserID, and Department, separated by spaces. The UserID itself consists of a two-character country code followed by a unique code of two or three digits. The objective is to extract only this unique numeric code from the UserID field.

Explanation of the Correct Function:

Option A: RIGHT(SUBFIELD(Key, ' ', 2), 3)

SUBFIELD(Key, ' ', 2):This function extracts the second part of the key (i.e., the UserID) by splitting the string using spaces as delimiters.

RIGHT(..., 3):After extracting the UserID, the RIGHT() function takes the last three characters of the string. This works because the unique code is either two or three digits, and the RIGHT() function will retrieve these digits from the UserID.

This combination ensures that the data architect extracts the unique code from the UserID field correctly.

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.

In the script provided, the alt() function is used to handle various date formats. The alt() function in Qlik Sense evaluates a list of expressions and returns the first valid expression. If none of the expressions are valid, it returns the last argument provided (in this case, '31/12/2022').

Step-by-step breakdown:

The alt() function checks the Date field for three different formats:

YYYYMMDD

YYYY/MM/DD

DD/MM/YYYY

If none of these formats match the value in the Date field, the default date '31/12/2022' is assigned.

Values in the Date field:

20210131: Matches the first format YYYYMMDD.

2020/01/31: Matches the second format YYYY/MM/DD.

31/01/2019: Matches the third format DD/MM/YYYY.

9999: Does not match any of the formats, so the alt() function returns the default value '31/12/2022'.