Free Qlik QSBA2024 Exam Dumps October 2026

When working with large datasets, such as the one containing over a billion rows in this scenario, loading the entire dataset can be time-consuming, especially during the development phase. Qlik Sense provides a Filter data option in the Data Manager, which allows business analysts to work with a subset of the data during development. This is particularly useful for rapidly prototyping the application design.

Key Concepts:

Filter Data Option: This feature in Qlik Sense allows the analyst to load a smaller sample of the dataset for analysis and development purposes. By filtering out unnecessary rows, the business analyst can quickly build and prototype the application without waiting for the full dataset to load. Once the design is finalized, the full dataset can be reloaded.

Prototyping with Reduced Data: It's often more efficient to work with a smaller dataset during the design phase. This allows for faster iterations and design cycles, especially when working with large datasets.

Why the Other Options Are Less Suitable:

A . Split the dataset and create a normalized star schema of associated tables: This would involve complex data modeling that is not necessarily related to the immediate need of reducing the size of the dataset for prototyping. While star schemas can optimize data models, it's not the quickest way to reduce the number of rows for initial testing.

B . Deselect text columns with unique data values to reduce the memory footprint: This may reduce the memory usage but won't necessarily address the issue of reducing the number of rows. Also, the text columns might be important for the analysis and should not be removed without careful consideration.

D . Truncate text fields longer than 256 characters to create preview fields: Truncating text fields will not significantly reduce the dataset size or the load time. It may also result in losing critical information, which is not ideal for prototyping.

References for Qlik Sense Business Analyst:

Rapid Prototyping: Qlik Sense encourages rapid development of applications by allowing business analysts to work with subsets of the data. The Filter Data option is an important tool for managing large datasets efficiently.

Data Manager Tools: The Data Manager in Qlik Sense provides several tools for reducing the dataset size, and filtering is one of the key options for improving performance during development.

Using the Filter data option allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes option C the most effective solution.

To make the price ranges dynamically adjustable by the application users, a variable input object is the best approach. The business analyst can define a variable that stores the range value, which users can modify directly through a variable input control. This method allows users to change the price ranges interactively, giving them control over the ranges used in the analysis.

Key Concepts:

Variable Input Object: This object allows users to interact with and modify the values of predefined variables directly within a Qlik Sense sheet, which can then be used to adjust calculations dynamically, such as defining custom ranges.

Configurable by Users: This approach gives end-users the flexibility to modify the visualization based on their specific needs without requiring backend changes or reloading the app.

Why the Other Options Are Less Suitable:

B . Create a calculated field in Data manager, using the Class() function: The Class() function is useful for creating static price ranges, but it does not allow for user interaction or dynamic updates to the ranges.

C . Load an independent source file to contain the user-defined boundary values: While this could provide configurable ranges, it's unnecessarily complex and would require more management, and it's not as user-friendly as using a variable.

D . Create the price range dimension using the Buckets feature in Data manager: Similar to the Class() function, this would create static groupings, which wouldn't be dynamically adjustable by the user.

References for Qlik Sense Business Analyst:

Dynamic Controls with Variables: Qlik Sense provides the ability to create variable input objects that allow users to control how visualizations behave dynamically. This feature is highlighted in Qlik Sense's Business Analyst best practices when making interactive applications.

By using a variable and a variable input object, the business analyst enables user interactivity and customization, making A the correct choice.

In Qlik Sense, default bookmarks allow a set of selections to be applied automatically whenever the app is opened. By creating a bookmark and setting it as the default, the business analyst ensures that the required selections are applied every time the app is opened, which meets the requirement of consistently applying the same selections for all users.

Key Concepts:

Default Bookmark: A default bookmark automatically applies the saved selections when an app is opened, ensuring consistency without manual input from users.

Bookmarking: This feature allows users to save specific selections or states of a dashboard for later use.

Why the Other Options Are Less Suitable:

A . Section Access: Section Access controls data access and security, not default selections.

B . Mashup with API: While this could technically work, it's unnecessarily complex and requires custom coding.

C . Sheet Action: A sheet action could apply a bookmark but would not ensure that the selections are applied at the time of app opening.

References for Qlik Sense Business Analyst:

Default Bookmark for Predefined Selections: This feature is commonly used to ensure that specific selections are always applied when an app is opened.

Thus, D is the best solution for applying default selections, making it the correct answer.

A line chart is the most appropriate visualization for comparing two continuous measures (Profit and Revenue) over time. Line charts are designed to show trends and patterns over a continuous scale (such as time), making it ideal for this scenario where we need to observe how both Profit and Revenue vary across a period.

Key Concepts:

Continuous Scale: Line charts are specifically suited for continuous data like time, making them the preferred choice when tracking changes over time for multiple measures.

Dual Measure Comparison: A line chart allows you to plot two measures on the same axis, making it easy to compare their trends over the same period.

Why the Other Options Are Less Suitable:

B . Bullet chart: A bullet chart is used to compare a single measure against a target, not for tracking two measures over time.

C . Bar chart: Bar charts are better suited for comparing categorical data, not continuous measures over time.

D . Scatter plot: Scatter plots are used to compare relationships between two measures but are not suited for continuous time-based comparisons.

References for Qlik Sense Business Analyst:

Line Charts for Time Series Data: Line charts are the recommended visualization for comparing multiple measures over time in Qlik Sense, especially when working with continuous data like Profit and Revenue.

Thus, the line chart is the best choice for this scenario, making A the correct answer.

To meet the needs of both the internal analyst team and the sales team, the best solution is to create two separate apps: one designed specifically for mobile use and another for internal use. Mobile devices require different UI considerations, such as simpler, touch-optimized layouts, while the internal team can benefit from a more detailed app optimized for desktop use. Designing separate apps ensures that both teams have a tailored experience that suits their specific devices and use cases.

Key Concepts:

Mobile Optimization: Mobile devices require apps that are streamlined and optimized for smaller screens, while internal users on desktop computers can handle more complex layouts and detailed reports.

Separate Apps: Creating separate apps ensures that each team gets the best user experience tailored to their needs.

Why the Other Options Are Less Suitable:

A . One app with a specific extension for mobile users: While extensions can provide some mobile functionality, they don't offer the flexibility and optimization needed for a fully mobile-friendly experience.

C . A mashup with various objects: A mashup may provide flexibility, but it could be overly complex for this requirement and wouldn't necessarily offer an optimal mobile experience.

D . One app with various objects: This could complicate the user experience for both teams, as mobile users may struggle with objects that are not optimized for their devices.

References for Qlik Sense Business Analyst:

Mobile vs. Desktop App Design: Qlik Sense recommends optimizing apps for specific devices to ensure the best user experience for both desktop and mobile users.

Thus, B is the correct answer because it provides the best solution for both the mobile sales team and the internal analyst team, making it the verified answer.

A scatter plot is the most appropriate visualization for analyzing the correlation between Product Sales ($), Order Volume, and Margin %. Scatter plots are ideal for showing relationships between two or more continuous variables, which is crucial for identifying trends or correlations among these measures.

Key Concepts:

Scatter Plot: This chart type is specifically designed to display correlations between measures, making it the ideal choice for visualizing relationships between Product Sales, Order Volume, and Margin %.

Multiple Measures: Scatter plots in Qlik Sense can plot two measures on the X and Y axes and can use colors or bubbles to represent additional measures (such as Margin %).

Why the Other Options Are Less Suitable:

A . Multi KPI: A Multi KPI displays multiple metrics but doesn't show correlations between them.

B . Combo chart: A combo chart combines bar and line charts but is not suited for analyzing correlations between multiple continuous measures.

D . Pivot table: While useful for data aggregation, a pivot table does not provide a clear visualization of correlations between measures.

References for Qlik Sense Business Analyst:

Scatter Plot for Correlation Analysis: Scatter plots are recommended in Qlik Sense when exploring relationships between multiple continuous variables.

Thus, the scatter plot is the most effective visualization for analyzing the correlation between Product Sales, Order Volume, and Margin %, making C the correct answer.

The app is experiencing performance issues due to inefficient calculations in a master measure that processes the field TransactionName, which has a complex structure (e.g., '1_ABCDEFGHI_23454'). Let's analyze the available options and why Option B is the best solution.

A . Ask the developer of the underlying database to change the structure of the field TransactionName.

While modifying the data structure in the underlying database might improve performance, this approach is not ideal. It's a time-consuming process that might not be feasible, especially when working with large datasets that have already been integrated into the Qlik Sense app. The performance improvement should focus on optimizing the Qlik app itself.

B . In the Data manager, use the Split function to split the field values with the underscore character as the separator. In the Data manager, use the Add calculated field function to multiply the 1st and the 3rd column of the split field. Reload the data.

This is the most efficient approach. By using the Split function in the Data Manager to break down the TransactionName field based on the underscore separator, the data becomes more accessible for calculations. You can then create a calculated field that multiplies the first and third components of the split data (corresponding to the 1st part and the numeric identifier at the end). This reduces the need for complex string manipulation functions (e.g., Left, Right, Trim) within the master measure, which can be resource-intensive when applied to large datasets.

C . Change the master measure definition as follows: subfield( TransactionName, '',!) * subfield( TransactionName, '', 3)

This option suggests using the subfield() function to split the string within the master measure itself. While this approach is valid, it doesn't provide as significant a performance improvement compared to pre-processing the data in the Data Manager. Calculating fields directly within the visualizations is more computationally expensive compared to handling it during the data load phase.

D . In the Data manager, use the Replace function to remove the middle part of the field TransactionName.

The Replace function would remove the middle section of the transaction name, but it doesn't address the need to split the field for efficient multiplication. It would also result in a loss of important data that may be required for other analyses.

Key Qlik Sense Business Analyst References:

The Data Manager provides powerful tools for transforming and optimizing data before it is used in visualizations. Pre-processing the data using functions like Split significantly reduces the load on front-end visualizations.

Splitting fields during the data load rather than in the master measures improves performance, especially in large datasets where string manipulation functions in visualizations can degrade performance.

Calculated fields allow analysts to create new expressions based on transformed data, ensuring that the app remains efficient while meeting analytical needs.

Thus, the correct solution is to use the Split function to separate the field values and then use a calculated field to multiply the required components, which enhances app performance.

In this Section Access script, the roles and access to data for different users are defined based on the SACOUNTRY field. Here's how the data access will work:

ADMIN: The ADMIN user has access to all data because the * in the SACOUNTRY field allows full access to all countries in the dataset.

USER1: This user has access to Spain and France because the SACOUNTRY field specifies these countries for USER1.

USER2: This user has access to United Kingdom because the SACOUNTRY field specifies only the UK for USER2.

Key Concepts:

Section Access: This feature in Qlik Sense controls which data users can see based on their login credentials. The access rights are controlled through fields like ACCESS, USERID, and SACOUNTRY in this case.

Why the Other Options Are Less Suitable:

B and C: These suggest that users won't see data they have access to, which contradicts the defined Section Access script.

D: This incorrectly assumes that ADMIN cannot see Germany, which is not defined in the script.

References for Qlik Sense Business Analyst:

Section Access Best Practices: In Qlik Sense, Section Access tables define the data that users can see, and the use of * for the ADMIN role ensures access to all data.

Thus, A is the correct answer because it matches the expected data access behavior based on the script, making it the verified answer.

In Qlik Sense, when dealing with inconsistent naming conventions across different systems or divisions (like the variation in country names), the best practice is to standardize the data during the loading process. Using a lookup table is the most efficient approach to achieve this. This involves loading a separate table that contains all variations of a country name along with the standardized version. During the load process, Qlik Sense can then map the varying names to a common value.

Key Concepts:

Lookup Table: A lookup table contains key-value pairs where different versions of a data element (like country names) are mapped to a single standard value. In this case, the lookup table could have entries like USA, U.S.A., US all mapped to United States of America.

Data Standardization: This is crucial in ensuring consistent analysis across datasets. By converting variations of country names into a single consistent value, the business analyst ensures that all data visualizations and analysis will treat 'USA', 'US', etc., as the same entity.

Why the Other Options Are Less Suitable:

A . Create a calculated master dimension expression: While this could theoretically work by creating a calculated expression to handle variations, it's not scalable or maintainable, especially as new variations in country names could appear in future data loads.

C . Cleanse the source text file prior to loading: This option would require modifying the raw data files manually, which is time-consuming and not sustainable if data is frequently updated or if the number of variations is extensive.

D . Use the Replace option in Data manager: The Replace option in the Data Manager could work on a small scale, but it requires manual intervention each time, which is not efficient or sustainable when new data is loaded. Also, it's more useful for one-off corrections than for handling systemic issues across multiple data loads.

References for Qlik Sense Business Analyst:

Data Modeling Best Practices: Lookup tables are a common approach to resolve issues of inconsistent data across multiple sources. They ensure that data is consistently represented in visualizations and reduce the need for manual intervention.

Data Cleansing During Loading: Qlik Sense allows for transformation and data cleansing during the data load process. A lookup table is part of this capability and ensures that the data loaded into the app is clean and consistent.

Using a lookup table is the most scalable and maintainable approach to standardizing country names in this scenario, which is why option B is the verified solution.