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Qlik QSDA2024 Exam Questions
- Topic 1: Identify Requirements: This section assesses the abilities of data analysts in defining key business requirements. It includes tasks such as identifying stakeholders, selecting relevant metrics, and determining the level of granularity and aggregation needed.
- Topic 2: Data Connectivity: This part evaluates how data analysts identify necessary data sources and connectors. It focuses on selecting the most appropriate methods for establishing connections to various data sources.
- Topic 3: Data Model Design: In this section, data analysts and data architects are tested on their ability to determine relevant measures and attributes from each data source.
- Topic 4: Data Transformations: This section examines the skills of data analysts and data architects in creating data content based on specific requirements. It also covers handling null and blank data and documenting Data Load scripts.
- Topic 5: Validation: This section tests data analysts and data architects on how to validate and test scripts and data. It focuses on selecting the best methods for ensuring data accuracy and integrity in given scenarios.
Free Qlik QSDA2024 Exam Actual Questions
Note: Premium Questions for QSDA2024 were last updated On Jul. 09, 2026 (see below)
A data architect receives an error while running script.
What will happen to the existing data model?
In Qlik Sense, when a data load script is executed and an error occurs, the script execution is halted immediately, and any tables that were being loaded at the time of the error are discarded. However, the existing data model---i.e., the last successfully loaded data model---remains intact and is not affected by the failed script. This ensures that the application retains the last known good state of the data, avoiding any partial or inconsistent data loads that could occur due to an error.
When the script encounters an error:
The tables that were successfully loaded prior to the error are retained in the session, but these tables are not merged with the existing data model.
The existing data model before the script was executed remains unchanged and is maintained.
No partial or incomplete data is loaded into the application; hence, the data model remains consistent and reliable.
Qlik Sense Data Architect Reference This behavior is designed to protect the integrity of the data model. In scenarios where script execution fails, the user can debug and fix the script without risking the data integrity of the existing application. The key references include:
Qlik Help Documentation: Provides detailed information on how Qlik Sense handles script errors, highlighting that the existing data model remains unchanged after an error.
Data Load Editor Practices: Best practices dictate ensuring that the script is fully functional before executing it to avoid data inconsistency. In cases where an error occurs, understanding that the current data model is maintained helps in strategic debugging and script correction.
Refer to the exhibit.
A company stores the employee data within a key composed of Country, UserlD, and Department. These fields are separated by a blank space. The UserlD field is composed of two characters that indicate the country followed by a unique code of two or three digits. A data architect wants to retrieve only that unique code.
Which function should the data architect use?
A)
B)
C)
D)
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.
Refer to the exhibit.
A data architect needs to build a dashboard that displays the aggregated sates for each sales representative. All aggregations on the data must be performed in the script.
Which script should the data architect use to meet these requirements?
A)
B)
C)
D)
The goal is to display the aggregated sales for each sales representative, with all aggregations being performed in the script. Option C is the correct choice because it performs the aggregation correctly using a Group by clause, ensuring that the sum of sales for each employee is calculated within the script.
Data Load:
The Data table is loaded first from the Sales table. This includes the OrderID, OrderDate, CustomerID, EmployeeID, and Sales.
Next, the Emp table is loaded containing EmployeeID and EmployeeName.
Joining Data:
A Left Join is performed between the Data table and the Emp table on EmployeeID, enriching the data with EmployeeName.
Aggregation:
The Summary table is created by loading the EmployeeName and calculating the total sales using the sum([Sales]) function.
The Resident keyword indicates that the data is pulled from the existing tables in memory, specifically the Data table.
The Group by clause ensures that the aggregation is performed correctly for each EmployeeName, summarizing the total sales for each employee.
Key Qlik Sense Data Architect Reference:
Resident Load: This is a method to reuse data that is already loaded into the app's memory. By using a Resident load, you can create new tables or perform calculations like aggregation on the existing data.
Group by Clause: The Group by clause is essential when performing aggregations in the script. It groups the data by specified fields and performs the desired aggregation function (e.g., sum, count).
Left Join: Used to combine data from two tables. In this case, Left Join is used to enrich the sales data with employee names, ensuring that the sales data is associated correctly with the respective employee.
Conclusion: Option C is the most appropriate script for this task because it correctly performs the necessary joins and aggregations in the script. This ensures that the dashboard will display the correct aggregated sales per employee, meeting the data architect's requirements.
A data architect implements Section Access on an app to reduce the data for each user when the user logs in. Each user is allowed to see their specific territory only.
The app is set for a scheduled reload every three hours. Without Section Access added, the app loads successfully. When Section Access is added and the script runs, the app fails to load.
What is causing this issue?
When implementing Section Access in Qlik Sense, it is crucial that all accounts that need to access the data---including the service account that performs the scheduled reload---are included in the Section Access table. If the service account is not included, Qlik Sense will not be able to access any data, leading to a failure in the reload process.
Here's a breakdown of why the other options are less likely:
A . The ACCESS column in the Section Access table has been added in lowercase: This would generally result in a syntax error, but it would not allow the script to execute successfully without causing an immediate failure, unrelated to Section Access.
C . A user name listed in the Section Access table is spelled incorrectly: While this could lead to some users not having the correct access, it would not cause the entire reload to fail. The issue here is broader, affecting the entire application load process.
D . The data architect does not have rights to reload the app: If the architect did not have rights, the script would not run successfully even without Section Access.
The correct issue in this scenario is that the service account running the task is not included in the Section Access table. This is a common cause of load failures after adding Section Access. To resolve this, ensure that the service account is added with sufficient privileges in the Section Access table
Exhibit.
While performing a data load from the source shown, the data architect notices it is NOT appropriate for the required analysis.
The data architect runs the following script to resolve this issue:
How many tables will this script create?
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 tables will 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 create 6 separate tables: one for each unique combination of Object and Attribute.
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.
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