Free Qlik QSDA2024 Exam Actual Questions

The questions for QSDA2024 were last updated On Mar 24, 2025

At ValidExamDumps, we consistently monitor updates to the Qlik QSDA2024 exam questions by Qlik. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Qlik Sense Data Architect Certification Exam - 2024 exam on their first attempt without needing additional materials or study guides.

Other certification materials providers often include outdated or removed questions by Qlik in their Qlik QSDA2024 exam. These outdated questions lead to customers failing their Qlik Sense Data Architect Certification Exam - 2024 exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Qlik QSDA2024 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.

 

Question No. 1

Exhibit.

A large electronics company re-assigns sales people once per year from one Department to another.

SPID is the Salesperson ID; the SPID for each individual sales person Name remains constant. The Department for a SPID may change; each change is stored in the Dynamic Dimension data.

Four tables need to be linked correctly: a transaction table, a dynamic salesperson dimension, a static salesperson dimension, and a department dimension.

Which script prefix should the data architect use?

Show Answer Hide Answer
Correct Answer: B

In the scenario described, the Dynamic Dimension data tracks changes in department assignments for salespeople over time. To correctly link the transaction data with the salesperson data and ensure that sales are associated with the correct department based on the date, an IntervalMatch function should be used.

IntervalMatch is designed to match discrete data (like transaction dates) with a range of dates. In this case, each salesperson's department assignment is valid over a period of time, and the IntervalMatch function can be used to link the transaction data with the correct department for each salesperson based on the transaction date.

Option A (Merge): This option is incorrect as it refers to combining data sets, which doesn't address the need to handle the dynamic, date-based department assignments.

Option B (IntervalMatch): This is the correct choice because it allows you to match each transaction with the correct department assignment based on the ChangeDate in the Dynamic Dimension data.

Option C (Partial Reload): This refers to reloading only part of the data, which is not relevant to linking tables based on date ranges.

Option D (Semantic): This option is not applicable as it refers to a broader approach to data modeling and interpretation rather than specifically linking data based on time intervals.

Thus, IntervalMatch is the correct method for linking the transaction data with the dynamic salesperson dimension, ensuring that each transaction is associated with the correct department based on the historical assignment data.


Question No. 2

A company's analytics team is migrating from QlikView to Qlik Sense. During the transition there is an opportunity to improve overall reporting.

Which set of criteria must the data architect consider while planning for the migration?

Show Answer Hide Answer
Correct Answer: C

During the transition from QlikView to Qlik Sense, the analytics team has the opportunity to improve the overall reporting. To ensure a smooth migration while optimizing the new environment, the data architect needs to consider several key factors.

Option C is the best choice because it encompasses the essential aspects of a migration project:

QlikView Archival:

Archiving QlikView applications is crucial to ensure that historical data and applications are preserved and can be referenced if needed in the future. This step is important to maintain continuity and provide a fallback option if required during the transition.

Source Data Architecture:

Understanding the existing source data architecture is critical to ensure that the new Qlik Sense applications can seamlessly connect to the data sources. This also helps in identifying opportunities to optimize or re-architect the data pipelines for better performance in Qlik Sense.

Load Script:

The load script from QlikView might need to be revised or optimized for Qlik Sense. It's important to ensure that the script is compatible and takes advantage of Qlik Sense's capabilities, such as improved data handling, better inline transformations, and enhanced scripting functions.

Data Model:

Reviewing and possibly redesigning the data model is essential during the migration. Qlik Sense's associative engine allows for more flexibility, and this is an opportunity to improve the data model for better performance, scalability, and user experience.

Business Use Case:

Understanding the business use case is vital to ensure that the new Qlik Sense applications meet the business requirements effectively. This includes making sure that the new reports and dashboards are aligned with the business goals and provide the necessary insights.


Qlik Migration Guide: When migrating from QlikView to Qlik Sense, it's important to consider not just the technical aspects but also the business implications and opportunities for improvement.

Qlik Documentation on Data Modeling and Load Script Optimization: These resources provide best practices on how to optimize load scripts and data models during migration to ensure smooth operation and better performance in Qlik Sense.

Question No. 3

Exhibit.

One of the data sources a data architect must add for a newly developed app is an Excel spreadsheet. The Region field only has values for the first record for the region. The data architect must perform a transformation so that each row contains the correct Region.

Which function should the data architect implement to resolve this issue?

Show Answer Hide Answer
Correct Answer: A

The given Excel spreadsheet has a Region field where the region value is only specified for the first record within each region. The data architect needs to fill in the missing region values for subsequent rows.

Previous() Function: The Previous() function in Qlik Sense returns the value of the expression from the previous row. In this case, it can be used to fill down the Region values so that each row contains the correct region information.

Implementation: The script can be designed to check if the current row's Region value is missing (null). If it is missing, the script can assign the value from the previous row using the Previous() function.

LOAD

If(IsNull(Region), Previous(Region), Region) AS Region,

...

This logic fills in the missing Region values with the value from the preceding row, which effectively resolves the issue shown in the spreadsheet.


Question No. 4

Exhibit.

A data architect must load the two tables without creating a synthetic key. The data architect also must make sure expressions like Sum([Budgeted Sales]) are calculated correctly.

Which load script meets these requirements?

A)

B)

C)

D)

Show Answer Hide Answer
Correct Answer: A

In the scenario provided, the data architect needs to load two tables (Budget and Sales) without creating a synthetic key, while ensuring that expressions like Sum([Budgeted Sales]) are calculated correctly.

Here is a breakdown of the options:

Option A (Outer Join): This option uses an outer join between the Sales table and the Budget table. While this approach will combine the tables based on the common fields (Year and Region), it will result in a single table that contains all fields from both tables. This approach prevents the creation of a synthetic key and retains all records from both tables, ensuring that all budgeted and actual sales data is available. As a result, calculations like Sum([Budgeted Sales]) will work correctly. This is the correct solution.

Option B (Concatenate): This option uses concatenate, which combines the tables by stacking them on top of each other as if they were one table. This approach will not prevent synthetic keys and could cause issues with calculations since Budgeted Sales and Actual Sales would be in the same column, leading to incorrect aggregation results.

Option C (Separate Load): This option simply loads the tables separately without any join or concatenation. While this will not create a synthetic key, it will result in two separate tables in the data model. Without any connection between these tables, calculations involving both Budgeted Sales and Actual Sales will not work correctly.

Option D (Inner Join): This option uses an inner join, which will combine only the records that match in both tables based on Year and Region. While this approach avoids synthetic keys, it may exclude records that do not have a corresponding match in both tables, potentially leading to incomplete data.

Given the requirements to avoid synthetic keys and ensure correct calculations, Option A (Outer Join) is the most appropriate approach. It ensures all relevant data is included and that the data model remains free from synthetic keys, while also allowing accurate calculations.


Question No. 5

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?

Show Answer Hide Answer
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