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Qlik QSDA2024 Exam Questions

Exam Name: Qlik Sense Data Architect Certification Exam - 2024
Exam Code: QSDA2024
Related Certification(s): Qlik Sense Certification
Certification Provider: Qlik
Actual Exam Duration: Minutes
Number of QSDA2024 practice questions in our database: 50 (updated: Apr. 13, 2025)
Expected QSDA2024 Exam Topics, as suggested by Qlik :
  • 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.
Disscuss Qlik QSDA2024 Topics, Questions or Ask Anything Related

Alline

17 days ago
Qlik Sense Data Architect exam conquered! Pass4Success made prep a breeze.
upvoted 0 times
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Izetta

1 months ago
Aced the Qlik certification! Pass4Success, thanks for the efficient study materials.
upvoted 0 times
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Deonna

2 months ago
Success on the Qlik Data Architect exam! Pass4Success, your questions were right on target.
upvoted 0 times
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Brittney

3 months ago
I passed the Qlik Sense Data Architect Certification Exam - 2024, and the Pass4Success practice questions were a great aid. One question that I found challenging was about data connectivity. It asked how to manage connections to cloud-based data sources, and I wasn't sure about the security implications.
upvoted 0 times
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Jolanda

3 months ago
Qlik certification in the bag! Couldn't have done it without Pass4Success's timely resources.
upvoted 0 times
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Hoa

4 months ago
Excited to share that I passed the Qlik Sense Data Architect Certification Exam - 2024! The practice questions from Pass4Success were very helpful. There was a question on data model design that asked about the best way to handle slowly changing dimensions, and I wasn't entirely confident in my answer.
upvoted 0 times
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Wynell

4 months ago
Passed the Qlik exam with flying colors! Pass4Success, you're the real MVP for last-minute prep.
upvoted 0 times
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Alease

4 months ago
I just passed the Qlik Sense Data Architect Certification Exam - 2024, and Pass4Success practice questions were invaluable. One difficult question was about data transformations, specifically how to handle data type conversions efficiently. I debated between using inline functions and pre-processing the data.
upvoted 0 times
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Desire

5 months ago
Thrilled to announce that I passed the Qlik Sense Data Architect Certification Exam - 2024! Pass4Success was a big help. A question on identifying requirements was tricky; it asked how to gather and document user requirements effectively, and I wasn't sure if I should focus more on interviews or surveys.
upvoted 0 times
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Carrol

5 months ago
Qlik Sense Data Architect certified! Pass4Success made it possible with their relevant exam questions.
upvoted 0 times
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Rebbeca

5 months ago
I successfully passed the Qlik Sense Data Architect Certification Exam - 2024, thanks to the practice questions from Pass4Success. One question that caught me off guard was about validation. It asked how to validate data accuracy after loading it into Qlik Sense, and I was unsure whether to use scripting or built-in functions.
upvoted 0 times
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Nell

6 months ago
Happy to share that I passed the Qlik Sense Data Architect Certification Exam - 2024! The Pass4Success practice questions were spot-on. There was a question on data connectivity that puzzled me. It was about the best practices for connecting to multiple data sources and ensuring data consistency.
upvoted 0 times
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Wilburn

6 months ago
Nailed the Qlik certification! Pass4Success materials were a lifesaver for quick prep.
upvoted 0 times
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Refugia

6 months ago
I passed the Qlik Sense Data Architect Certification Exam - 2024, and Pass4Success practice questions were a great resource. One challenging question was about designing a data model. It asked how to optimize the model for performance while ensuring data integrity, which made me think hard about normalization techniques.
upvoted 0 times
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Benton

7 months ago
The exam tested understanding of data modeling for specific visualization types. Practice optimizing data models for different chart requirements.
upvoted 0 times
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Karl

7 months ago
Just cleared the Qlik Sense Data Architect Certification Exam - 2024! Thanks to Pass4Success, I felt well-prepared. There was a tricky question on data transformations, specifically about the best method to handle null values in a dataset. I hesitated between using a conditional function and a mapping table.
upvoted 0 times
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Quentin

7 months ago
Finally certified! Last tip: study up on data versioning and change management practices in Qlik Sense environments.
upvoted 0 times
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Jacqueline

7 months ago
Just passed the Qlik Sense Data Architect exam! Thanks Pass4Success for the spot-on practice questions.
upvoted 0 times
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Lili

7 months ago
I recently passed the Qlik Sense Data Architect Certification Exam - 2024, and I must say, the Pass4Success practice questions were incredibly helpful. One question that stumped me was about identifying the key requirements for a data model. It asked how to prioritize business needs over technical constraints, and I wasn't entirely sure of the best approach.
upvoted 0 times
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Free Qlik QSDA2024 Exam Actual Questions

Note: Premium Questions for QSDA2024 were last updated On Apr. 13, 2025 (see below)

Question #1

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)

Reveal Solution Hide Solution
Correct Answer: A

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.


Question #2

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)

Reveal Solution Hide Solution
Correct Answer: C

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.


Question #3

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?

Reveal Solution Hide Solution
Correct Answer: B

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


Question #4

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?

Reveal Solution Hide Solution
Correct Answer: D

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.

Question #5

A data architect executes the following script:

What will be the result of Table.A?

A)

B)

C)

D)

Reveal Solution Hide Solution
Correct Answer: D

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

Table_B contains 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



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