Most Recent Qlik QSBA2024 Exam Questions & Answers

A Network chart is the most suitable visualization for representing processes and tasks that have dependencies, such as projects with co-dependent tasks and subtasks. The network chart allows you to visualize relationships between nodes (in this case, tasks and subtasks) and can display them in a structured manner with lines connecting them based on their relationships. The colors of the boxes (or nodes) can be determined by the status of each project or task, which matches the requirements.

Key Concepts:

Network Chart: It's designed for showing interconnections or relationships between various elements. It is ideal when tasks or processes have dependencies or subtasks that need to be visually represented with links between them.

Color Representation: In a Network Chart, you can easily apply colors to nodes based on specific criteria, such as the status of the task, making it easier for users to track project progress at a glance.

Why the Other Options Are Less Suitable:

A . Sankey chart: While Sankey charts are used to show flow and relationships between categories, they are better suited for representing flows of data or values between stages, not hierarchical or task-related dependencies.

B . Grid chart: A grid chart is used to display values in a matrix but does not provide the relational and hierarchical representation needed for tasks and subtasks.

C . Org chart: Org charts are useful for showing hierarchies but are more structured for organization personnel or roles rather than co-dependent tasks and workflows.

References for Qlik Sense Business Analyst:

Network Diagram: Network charts are widely used for visualizing complex relationships between entities, which aligns with the need to visualize tasks and subtasks in project management.

Thus, a Network chart provides the best solution for visualizing tasks and subtasks with their dependencies, making D the correct answer.

To meet the CEO's requirements for a dynamic, interactive, and visually impactful presentation that can also be shared, the Storytelling feature in Qlik Sense is the best option. The Storytelling feature allows the business analyst to create a narrative combining static snapshots and live Qlik Sense sheets. This is perfect for large presentations as it lets the CEO present critical information and answer audience questions dynamically by embedding live sheets into the story, making the presentation interactive.

Key Concepts:

Storytelling: This feature allows the creation of a sequence of slides that include embedded live Qlik Sense visualizations, enabling dynamic responses to audience questions.

Shared Content: Once the app is published, it can be shared with a broader audience, allowing stakeholders to interact with the embedded sheets.

Why the Other Options Are Less Suitable:

A . Embed a video player: While videos can be useful, they do not allow for dynamic interaction, which is a requirement.

B . Colorful and layered visualizations exported as PDF: PDFs are static, and exporting as a PDF would not allow for dynamic interaction during the presentation.

C . NPrinting slideshow-style report: NPrinting is useful for generating reports, but it does not meet the need for live, dynamic presentations.

References for Qlik Sense Business Analyst:

Qlik Sense Storytelling for Presentations: Storytelling allows users to blend narrative and interactive elements, making it ideal for presenting to large audiences.

Thus, D is the best choice because it meets all the requirements for interactive and dynamic presentations, making it the verified answer.

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.

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 case, we need to count how many unique rodents have made at least one attempt at any maze. Since the relationship between the Rodent table and the MazeEscapes table is a one-to-many relationship (each rodent may attempt several mazes), the best approach is to base the calculation on the MazeEscapes table, as it holds the actual records of rodent attempts.

By creating a new field RodentID_Counter in the MazeEscapes table, the analyst ensures that each rodent's attempt is captured. Using the Count (Distinct RodentID_Counter) as the KPI expression then accurately counts each unique rodent that has made at least one attempt, without counting multiple attempts by the same rodent.

Key Concepts:

Distinct Count: The distinct count ensures that rodents who have made multiple maze attempts are only counted once.

MazeEscapes Table: This table holds the records of each attempt, making it the correct source for this KPI.

Why the Other Options Are Less Suitable:

B . Sum (RodentID_Counter) in the Rodent table: This would incorrectly sum the number of rodents rather than counting unique attempts, leading to inaccurate results.

C . Sum (RodentID_Counter) in the MazeEscapes table: Summing the counter would not account for the distinct rodents, leading to incorrect counting.

D . RodentID_Counter in the Rodent table: This would not correctly reflect the rodents that have made an attempt, as not every rodent in the Rodent table may have attempted a maze.

References for Qlik Sense Business Analyst:

Distinct Count in KPI: It is best practice to use Count(Distinct ...) when working with KPIs that require unique counts, such as counting distinct rodents in this scenario.

Thus, A is the verified answer because it ensures that each rodent is counted once, based on their attempts in the MazeEscapes table.