Qlik Qlik Sense Data Architect Certification Exam – 2024 QSDA2024 Exam Dumps: Updated Questions & Answers (October 2025)

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 issue arises from using the applymap() function to normalize the country field values, but the result is incorrect. The reason is most likely related to the values in the source mapping table not matching the values in the Fact_Table properly.

The applymap() function in Qlik Sense is designed to map one field to another using a mapping table. If the source values in the mapping table are inconsistent or incorrect, the applymap() will not function as expected, leading to incorrect results.

Steps to resolve:

Review the mapping table (MAP_COUNTRY): The country field in the CountryTable contains values such as "U.S.", "US", and "United States" for the same country. To correctly normalize the country names, you need to ensure that all variations of a country's name are consistently mapped to a single value (e.g., "USA").

Apply Mapping: Review and clean up the mapping table so that all possible variants of a country are correctly mapped to the desired normalized value.

Key References:

Mapping Tables in Qlik Sense: Mapping tables allow you to substitute field values with mapped values. Any mismatches or variations in source values should be thoroughly reviewed.

Applymap() Function: This function takes a mapping table and applies it to substitute a field value with its mapped equivalent. If the mapped values are not correct or incomplete, the output will not be as expected.

In the provided scenario, the goal is to export tables from a Qlik Sense model based on rules specified in an external text file. The structure of the text file indicates which table to export, the filename to use, and how many copies to create.

Given this structure, the data architect needs to:

Loop through each row in the text file to process each table.

Use an IF statement to check whether the specified table exists in the model (though it's mentioned they are verified to exist, this step may involve conditional logic to ensure the rules are correctly followed).

Use another IF statement to handle the creation of multiple copies, ensuring each file is named incrementally (e.g., Clients1.qvd, Clients2.qvd, etc.).

Key Script Strategies:

Loop: A loop is necessary to iterate through each row of the text file to process the tables specified for export.

IF Statements: The first IF statement checks conditions such as whether the table should be exported (based on additional logic if needed). The second IF statement handles the creation of multiple copies by incrementing the filename.

This approach covers all the necessary logic with the minimum set of scripting strategies, ensuring that each table is exported according to the rules defined.

When developing multiple Qlik Sense applications (Sales, Finance, Operations) that share numerous identical calculation expressions, it is crucial to have a centralized, reusable script to avoid redundancy, save time on modifications, and increase the reusability of the assets.

The best approach in Qlik Sense to achieve these goals is to use theIncludescript function. This function allows the data architect to reference a script file that is stored on a file server. The Include function willinject the contents of the external script file into the Qlik Sense script at the point where the Include statement is called. This means that all three apps (Sales, Finance, Operations) can include this common script, and any updates made to the script will automatically apply to all apps that include it.

This method provides a highly maintainable solution because:

No Duplicate Script:The shared logic is maintained in a single file, eliminating redundancy.

Ease of Modifications:Any changes made to the script are propagated to all applications that include it.

Reusable Assets:The script can be reused across different applications, enhancing efficiency and consistency.

In this scenario, the Section Access security table controls user access to data within the Qlik Sense app. The user in question, ABC\PPP, has a specific entry in the security table that determines their access rights to the LEVEL field.

Understanding Section Access:

Section Accessis used to enforce security by restricting access to certain data based on the user's credentials.

In the security table provided, the USER role for ABC\PPP is set to have access to all data (* in the LINK field), but the OMIT field is set to LEVEL. The OMIT field in Section Access specifies fields that should be omitted from the user's view.

Outcome:

Since the OMIT field for user ABC\PPP is set to LEVEL, this user will not have access to the LEVEL field in the Qlik Sense application.

Option D: The table is displayed without the LEVEL columnis the correct outcome.

Explanation: When user ABC\PPP opens the app, the LEVEL field is omitted from their view. Any table or visualization that uses the LEVEL field will have that field excluded from display. The rest of the data and columns in the table will be visible, but the LEVEL column will not be shown.

References:

Qlik Sense Security and Section Access Documentation: The OMIT functionality in Section Access is specifically designed to remove fields from the user's access, ensuring that sensitive or unnecessary data is not exposed.

In this scenario, the data architect is loading two tables, Orders and Shipments, into Qlik Sense, and a synthetic key is being generated due to the presence of shared fields (OrderID and LineNo) between these tables.

Understanding the Issue:

Synthetic Keys: Qlik Sense automatically creates synthetic keys when two or more tables share multiple fields with the same names. While synthetic keys aren't necessarily problematic, they can sometimes lead to incorrect or unexpected data associations and should be resolved when possible to maintain clarity and control over the data model.

The tables Orders and Shipments share the fields OrderID and LineNo. In this context, these fields together uniquely identify each record, so they are both necessary for accurate data linkage.

Correct Resolution Approach:

Option C: Create a composite key using OrderID and LineNois the best approach.

Here's why:

Composite Key Creation:

By creating a composite key that combines OrderID and LineNo (e.g., OrderID & '-' & LineNo), you ensure that each line in the orders and shipments tables is uniquely identified. This composite key will accurately link the related records from the Orders and Shipments tables.

Avoiding Synthetic Keys:

By manually creating this composite key, you eliminate the need for Qlik Sense to generate a synthetic key, thereby simplifying the data model and ensuring that data associations are clear and controlled.

Retaining Both Fields:

This approach allows you to keep both OrderID and LineNo as separate fields in your tables if needed for other analyses or reporting purposes, while using the composite key for linking the tables.

References:

Qlik Sense Data Modeling Best Practices: When dealing with multiple fields that are used together to uniquely identify records, it is recommended to create composite keys rather than relying on Qlik Sense's synthetic keys for clarity and better control.

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.

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.

The scenario describes an app that is experiencing long load times due to the need to pull all records, both new and historical, from three large databases. This situation puts a strain on both the Qlik environment and the source databases. Given that all data is required for analysis, a full reload each time can be inefficient and resource-intensive.

Implementingincremental loadis a widely recommended approach in such cases. Incremental loading allows you to load only new or changed data since the last reload, rather than reloading all the data every time. This significantly reduces the time and resources required for reloading, as only a subset of the data needs to be processed during each reload. QVD (QlikView Data) files are typically used to store the historical data, while only the new or updated records are fetched from the source databases.

This approach would help:

Reduce the load on the source databases.

Shorten the data reload window.

Maintain historical data efficiently while ensuring that all new data is captured.

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.