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QlikView QSBA2024 Exam Prep
Qlik Sense Business Analyst Certification - 2024 (Page 3 )

Updated On: 20-Aug-2026
Page 2 of 11
PDF with 50 Questions

Two customers in an organization want to use an app that contains a finance data set. With different analysis objectives, each customer will only use a subset of that data.
Which procedure should the business analyst follow?

  1. Apply Section Access to manage the data for each customer
  2. Create multiple visualizations using set analysis
  3. Duplicate and rename the apps for each customer
  4. Unpivot, then re-associate the data tables for each customer

Answer(s): B

Explanation:

In Qlik Sense, Set Analysis is one of the most powerful tools available to a Business Analyst for managing different subsets of data within the same app. Since both customers are working with the same finance dataset but have different objectives, creating multiple visualizations using set analysis allows the analyst to tailor the data views for each customer without duplicating the app or creating complex data models.
Key Concepts:
Set Analysis: This feature enables the creation of expressions that define subsets of data, allowing you to filter data within specific visualizations. This is ideal when multiple users need different insights from the same underlying dataset.
Flexibility: Using set analysis, you can specify conditions within individual visualizations so that each user can focus on their own segment of the data without impacting others. Efficiency: This method avoids redundancy by ensuring you only need one app and one data model, instead of duplicating and maintaining multiple apps or applying complex logic such as Section Access.
Why the Other Options Are Less Suitable:
A . Apply Section Access: While Section Access is useful for managing security and limiting what users can see in the entire dataset, it is primarily designed to restrict data access based on user roles. In this case, both users need access to the same dataset but will conduct different analyses. Section Access would be an overly restrictive and complex solution for this scenario. C . Duplicate and rename the apps: This is inefficient because it leads to redundancy and makes maintenance harder (e.g., any changes to the dataset or visualizations would need to be applied to both apps). It also increases the risk of inconsistencies across versions of the app.
D . Unpivot and re-associate the data tables: This option is not relevant to the problem, as unpivoting is more appropriate for transforming datasets rather than tailoring views for different users within the same app. It does not address the need for customer-specific analysis objectives.
References for Qlik Sense Business Analyst:
Set Analysis: In the Qlik Sense Business Analyst's toolkit, Set Analysis is covered as a method to manage diverse data subsets within single apps, providing the flexibility needed in multi-user environments without duplicating content.
Efficient Application Design: Best practices suggest maintaining a single app where possible to ensure consistency and ease of maintenance, which aligns with the approach of using Set Analysis. By using Set Analysis, you provide both customers with tailored data views that are easily managed and updated within a single app. This is why option B is the most effective and verified solution.



An app needs to load a few hundred rows of data from a .csv text file. The file is the result of a concatenated data dump by multiple divisions across several countries. These divisions use different internal systems and processes, which causes country names to appear differently. For example, the United States of America appears in several places as 'USA', 'U.S.A.', or 'US'. For the country dimension to work properly in the app, the naming of countries must be standardized in the data model.
Which action should the business analyst complete to address this issue?

  1. Create a calculated master dimension expression
  2. Load a lookup table to convert values
  3. Cleanse the source text file prior to loading
  4. Use the Replace option in Data manager

Answer(s): B

Explanation:

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.



A business analyst needs to rapidly prototype an application design for a prospective customer. The only dataset provided by the customer contains 30 fields, but has over one billion rows. It will take too long to keep loading in its entirety while the analyst develops the data model.
Which action should the business analyst complete in the Data manager?

  1. Split the dataset and create a normalized star schema of associated tables
  2. Deselect text columns with unique data values to reduce the memory footprint
  3. Use the Filter data option to reduce the number of rows
  4. Truncate text fields longer than 256 characters to create preview fields

Answer(s): C

Explanation:

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.



A business analyst designs a visualization to analyze a count of products by fixed price ranges. The customer now wants the price ranges to be dynamically modified so they are configurable by the application users. The business analyst modified the dimension axis on the visualization.
Which step should the business analyst complete next?

  1. Define a variable and use it in a variable input object on the sheet
  2. Create a calculated field in Data manager, using the Class() function
  3. Load an independent source file to contain the user-defined boundary values
  4. Create the price range dimension using the Buckets feature in Data manager

Answer(s): A

Explanation:

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.



A business analyst needs to create two side-by-side charts for a sales department with the following data:
· Number of orders
· Name of the customer
· Percentage of margin
· Total sales
The charts use a common dimension, but each chart has different measures. The analyst needs to create a color association between the two charts on the dimension values.
Which action should the business analyst take?

  1. Use nested IF statements to set the colors by expression for each dimension value
  2. Define the color values in the master measures and use the color library
  3. Select 'By Dimension' and 'Persistent colors' in the Colors property panel
  4. Use the Fieldlndex function to set the colors by expression for each dimension value

Answer(s): C

Explanation:

In Qlik Sense, the 'By Dimension' and 'Persistent colors' options in the Colors property panel ensure that the same dimension values have the same color across multiple charts. This is especially useful when you have two or more side-by-side charts sharing a common dimension, like customer names in this case. Persistent colors guarantee consistency in color assignment, helping users visually track the same dimension across different visualizations.
Key Concepts:
By Dimension: This option ensures that each unique value of a dimension (e.g., customer name) gets a distinct color across all charts that use this setting. Persistent Colors: This feature ensures that the colors remain the same between charts, making the visual comparison across charts easier for the users.
Why the Other Options Are Less Suitable:
A . Use nested IF statements to set the colors by expression for each dimension value: While this would work, it would be unnecessarily complex to maintain and manage, especially with many dimension values.
B . Define the color values in the master measures and use the color library: This would only apply if the goal was to set colors based on measures, not dimensions. In this case, dimension consistency is required, not measure-based coloring.
D . Use the FieldIndex function to set the colors by expression for each dimension value: This would involve writing complex expressions that would not be as straightforward as using the built-in functionality of 'By Dimension' and 'Persistent colors'.
References for Qlik Sense Business Analyst:
Color Consistency Across Charts: The 'By Dimension' and 'Persistent colors' settings are recommended in Qlik Sense documentation when creating multi-chart layouts with shared dimensions, ensuring visual coherence across different charts. The Persistent colors and By Dimension settings offer a straightforward and maintainable way to create color associations across charts, making option C the verified solution.



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