QSBA2024 Sample Questions Answers

Aline chartis 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, theline chartis the best choice for this scenario, makingAthe correct answer.

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.

In this scenario, the VP of Sales wants to see the total sales for the year 2022regardless of selections. This requirement is best handled usingSet Analysisin Qlik Sense. The key to achieving this is using the set identifier 1, which ignores the user's selections and ensures the calculation is based on the entire dataset. The expression Sum( { 1 < year = { '2022' } > } Sales) calculates the sum of sales for the year 2022, irrespective of any other selections made in the app.

Key Concepts:

Set Identifier 1: This identifier ensures that the calculation is performed on the entire dataset, ignoring any selections or filters applied by the user.

Year Filtering: The expression < year = { '2022' } > ensures that only sales from the year 2022 are included in the calculation.

Why the Other Options Are Less Suitable:

A. Sum( { < year = '2022' > } Sales): This is incorrectly formatted, and the pipe (|) symbol should not be used in this context.

B. Sum( { $ < year = { '2022' } > } Sales): The $ set identifier respects the current selections, which is not what is needed in this case, as the VP wants the value to be shown regardless of selections.

D. Sum( 1 { < year = { '2022' } > } Sales): The placement of 1 is incorrect in this expression.

References for Qlik Sense Business Analyst:

Set Analysis: Qlik Sense's Set Analysis allows analysts to control what data is used in a calculation, regardless of user selections. The set identifier 1 ensures that selections are ignored, which is essential for showing fixed data such as total sales for a specific year.

Thus, the correct expression to show the total sales for 2022, regardless of user selections, isC.

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 aFilter dataoption 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 theFilter dataoption allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes optionCthe most effective solution.

In Qlik Sense, when leadership requests a high-level summary such as thetotal cost of delivery overall, the most efficient way to present this information is by using aKPI object. The KPI object is specifically designed to display a single, important metric in a simple and clear format.

A. Use the line chart to add up each month-year to get to the number required.This option is not efficient because it requires manual effort to add up the values from the line chart for each period. Additionally, this method is prone to human error and would be time-consuming during a presentation.

B. Create a KPI object that shows the total cost of delivery.The most appropriate action here is to use aKPI objectto display the overall total cost of delivery. A KPI in Qlik Sense is specifically designed to display single, aggregate measures in a clean and concise way, making it the perfect choice for presenting high-level summaries to leadership.

C. Adjust the line layer on the map to reflect the cost of delivery.While it is possible to adjust the map, the map is primarily used for spatial analysis. Modifying it to reflect the overall cost of delivery would not be as intuitive or effective as using a KPI object. Additionally, it could lead to unnecessary clutter and confusion for the audience.

D. Select all products in the scatter plot to see the total delivery cost.Selecting all products in the scatter plot would not give the desired result because the scatter plot is designed to show relationships between variables (cost of delivery and time-to-deliver). It’s not ideal for displaying aggregate values like total cost.

Key Qlik Sense Business Analyst References:

KPI objectsare ideal for presenting single, key metrics such as the total cost of delivery. They provide a straightforward, visually clear representation of high-level performance indicators.

Best practices in dashboard developmentemphasize the importance of creating specific visualizations that address both granular and high-level data needs. KPI objects fulfill the need for high-level summaries, particularly in leadership presentations.

Thus, the best way to show the total cost of delivery to leadership is tocreate a KPI object.

In Qlik Sense, creatingMaster Itemsallows business analysts to define fields, dimensions, and measures that are used consistently across the app. This is particularly important for measures that will be used repeatedly. By defining these as master items, you ensure that all calculations are consistent and optimized for user experience and performance. This approach also supports drill-down capabilities while ensuring a responsive user experience.

Key Concepts:

Master Items: Master Items are reusable definitions for dimensions, measures, and visualizations. When you create a measure as a Master Item, it becomes available for use across different visualizations, ensuring consistency and reducing duplication of effort.

User Experience and Load Time: Using Master Items optimizes performance, as Qlik Sense caches the results of these items, reducing the need for recalculating complex measures each time they are used.

Why the Other Options Are Less Suitable:

A. Aggregate the data to the source period: While aggregation could reduce the data volume, it would limit the ability to drill down to the detailed levels required by the users.

C. Combine the various source fields in a calculated item in the Data manager: While you could create calculated fields, this method would be less efficient than defining measures in the Master Items library. Calculations done outside Master Items would need to be manually repeated in each visualization, leading to inconsistencies.

D. Combine the source fields and create additional fields in Excel: This would not optimize user experience or load time, as it relies on modifying source data outside of Qlik Sense and could lead to unnecessary data duplication and inefficiencies.

References for Qlik Sense Business Analyst:

Master Items Best Practices: Qlik Sense promotes the use of Master Items for consistent measure definition and reuse, as they improve performance and ensure consistency across multiple visualizations.

By creating a Master Item, the business analyst ensures a streamlined and efficient user experience, makingBthe best and verified option for this scenario.

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

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

USER1: This user has access toSpainandFrancebecause theSACOUNTRYfield specifies these countries for USER1.

USER2: This user has access toUnited Kingdombecause theSACOUNTRYfield 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 likeACCESS,USERID, andSACOUNTRYin 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 definedSection Accessscript.

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

Abox plotis the best visualization for displaying the distribution of salaries broken down by department with standard deviation indicators. Box plots show the spread of data, including key measures like quartiles, median, and outliers, which are useful for analyzing salary distributions. They also naturally incorporate standard deviation indicators through the spread of data.

Key Concepts:

Box Plot: This type of chart is designed for analyzing the distribution of data across different categories (in this case, departments). It shows the spread and variability of data, which can include standard deviations.

Why the Other Options Are Less Suitable:

A. Distribution plot: While a distribution plot can show spread, it’s not as effective for showing standard deviation and is less suited for categorical breakdowns.

C. Histogram: A histogram shows the distribution of a single variable, but it doesn’t provide the same detailed breakdown as a box plot.

D. Scatter plot: Scatter plots are used for showing relationships between two variables and are not suitable for showing standard deviation across departments.

References for Qlik Sense Business Analyst:

Box Plot for Distribution Analysis: Box plots are ideal for visualizing data distribution and variability across categories, making them the preferred choice for analyzing salary distribution by department.

Thus, thebox plotis the best choice for visualizing salary distribution with standard deviation indicators, makingBthe verified answer.

To dynamically highlight a specific product bar in a bar chart based on conditions, the best approach is to use theColor By Expressionfeature under the Appearance settings in Qlik Sense. This feature allows you to apply conditional formatting to bars, changing their color dynamically based on expression logic.

A. A Color By Expression property under AppearanceThis is the correct answer. TheColor By Expressionproperty allows the business analyst to dynamically color bars in the chart based on specific conditions. The expression can be set to highlight a specific product bar only when certain conditions are met, and the color can be customized to make it stand out.

B. A Dimension reference line add-onDimension reference lines are used to show thresholds or important values along the axes, but they do not dynamically color the bars or add conditional labels to them. This would not achieve the desired effect.

C. An Alternative dimension with a calculationAlternative dimensions allow users to switch between different dimensions in the same chart, but they do not provide dynamic highlighting or conditional visibility for labels.

D. A reference line add-on under PropertiesReference lines are used to mark specific values or thresholds in a chart, but they do not interact with the dynamic coloring or visibility of labels on individual bars.

Key Qlik Sense Business Analyst References:

Color By Expressionis a powerful feature in Qlik Sense that allows dynamic customization of chart colors based on expressions, making it ideal for highlighting specific data points or conditions.

This feature provides great flexibility in creating visually engaging and interactive charts that respond to changes in the underlying data or user selections.

Thus, the correct way to highlight a specific product bar with a dynamic label is to useColor By Expression under Appearance.

Ascatter plotis the best choice for visualizing outliers, clustering, and products that are above the median sales volume. Scatter plots are excellent for showing relationships between two variables (like sales volume and margin %) while providing the ability to highlight outliers. By usingK-Means clusteringto color the products, the business analyst can visually group similar products. Additionally, adding amedian sales volume reference linemakes it easy to identify which products are above or below the median.

Key Concepts:

Scatter Plot: This visualization is ideal for detecting outliers and understanding the distribution and clustering of data points.

K-Means Clustering: This technique groups similar data points (products) based on their values, which helps to identify patterns visually.

Median Reference Line: Adding a reference line for median sales volume ensures that the analyst can easily see which products are performing above or below the median.

Why the Other Options Are Less Suitable:

A. Pivot table: While a pivot table could show aggregated data, it is not as effective for visualizing outliers or clustering as a scatter plot.

B. Treemap: A treemap can show hierarchical relationships but is not as effective for identifying clusters or outliers.

C. Combo chart: A combo chart is not as well-suited for visualizing clustering and outliers as a scatter plot.

References for Qlik Sense Business Analyst:

Outliers and Clustering Visualization: Qlik Sense recommends scatter plots for visualizing relationships between two measures and for detecting outliers and clustering in the data.

Thus, creating ascatter plotwith K-Means clustering and a median sales volume reference line is the best approach, makingDthe verified answer.

ANetwork chartis 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, aNetwork chartprovides the best solution for visualizing tasks and subtasks with their dependencies, makingDthe correct answer.

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 theMazeEscapestable, as it holds the actual records of rodent attempts.

By creating a new fieldRodentID_Counterin theMazeEscapestable, the analyst ensures that each rodent’s attempt is captured. Using theCount (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,Ais the verified answer because it ensures that each rodent is counted once, based on their attempts in the MazeEscapes table.