QSBA2024 Qlik Sense Business Analyst Certification Exam-2024 Question and Answers

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 efficiently categorize support cases based on age (New, Aging, Beyond SLA) for use in multiple places across the dashboard, theBucket option in the Data Manageris the most efficient approach. Bucketing allows the business analyst to create new categories based on the values in an existing field (in this case, the age of support cases). Since the dataset is expected to grow, creating the categories directly within Qlik Sense ensures that the process is scalable without the need for external tools or extensive coding.

Key Concepts:

Bucket Function: This allows you to group numeric fields into predefined ranges or categories. The function is highly scalable, making it suitable for large datasets.

Efficiency: Creating a new field using Bucketing ensures that the categorization is done directly in the app, avoiding the need for external data sources or nested IF statements, which could impact performance.

Why the Other Options Are Less Suitable:

A. Ask the ServiceNow team to create the field: This would create a dependency on external teams and could delay the development process.

B. Create an Excel sheet: This adds unnecessary complexity and isn’t scalable as the dataset grows.

D. Write a master dimension with a nested IF statement: While this could work, it's less efficient for handling large datasets and could result in slower performance.

References for Qlik Sense Business Analyst:

Bucketing Data: Qlik Sense recommends using the Bucketing feature for creating predefined ranges or categories, especially when dealing with large datasets.

Thus, using theBucket optionto create a new field for categories is the most efficient solution, makingCthe correct answer.

In this scenario, the second measure (Count of MajorCities) is likely not being displayed because the two measures—Sum(Knowing sections)andCount(MajorCities)—are on vastly different scales. When two measures have significantly different ranges, one of them may not be visible on the same Y-axis, causing the issue you're seeing where no bars are visible for the second measure.

By converting the bar chart to acombo chart, the business analyst can display both measures with appropriate configurations. The combo chart allows you to display different measures in different ways, such as using one axis for the first measure (e.g., bars forSum(Knowing sections)) and another axis for the second measure (e.g., bars forCount(MajorCities)), ensuring that both are visible on the chart.

Key Concepts:

Combo Chart: This type of chart allows you to display multiple measures using different axis scales or types of visualization (e.g., bars and lines).

Scale Mismatch: When two measures differ significantly in scale, they may not be displayed properly on the same axis. A combo chart helps by allowing separate Y-axes for each measure.

Why the Other Options Are Less Suitable:

A. Enable Value labels: While value labels can help show specific data points, they won't resolve the issue of one measure being invisible due to scale differences.

B. Recreate as an alternative measure: This would allow switching between measures, but the requirement is to show both measures simultaneously.

C. Change Y-axis Range to Custom: While adjusting the Y-axis manually might help, it’s not the best solution because the scale difference between the two measures might still cause issues, and it would be harder to adjust dynamically.

References for Qlik Sense Business Analyst:

Combo Charts for Multiple Measures: Combo charts are recommended in Qlik Sense when you need to display multiple measures with different scales.

Thus, converting the bar chart to acombo chartensures both measures are properly displayed, makingDthe correct answer.

Ascatter plotis the most appropriate visualization for analyzing the correlation betweenProduct Sales ($),Order Volume, andMargin %. 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, thescatter plotis the most effective visualization for analyzing the correlation between Product Sales, Order Volume, and Margin %, makingCthe correct answer.

In scenarios where a customer needs to standardize colors across multiple apps and be able to update them quickly, usingvariablesin combination with aninclude statementis the most flexible and maintainable approach.

A. Design all base objects as master visualizations and link each object in each app to the relevant master visualization.While master visualizations help with consistency within a single app, they don't offer an easy way to update all apps globally. You would need to manually update the colors in every master visualization in each app, which is not efficient for large-scale management.

B. Develop the first app with every variation of object and visualization and duplicate this app.Duplicating apps will create maintenance challenges. Each app would need to be updated individually if colors or other settings change, which is not scalable for over 100 apps.

C. Create all color expressions as variables in a text file, load it in each app with an include statement, and use these variables in the color property of all objects.This is the most efficient solution. By storing color definitions in a text file and loading them with aninclude statement, the business analyst can update the colors in one place, and these updates will be reflected across all apps that use the file. This method ensures easy maintenance and flexibility.

D. Store color definitions within a .qvd file and load it as a data island.While using a .qvd file is possible, it’s not as straightforward as using variables and an include statement. Data islands are typically used for selection purposes, and this method would introduce unnecessary complexity in managing colors.

Key Qlik Sense Business Analyst References:

Variablesare widely used in Qlik Sense for managing repeated expressions or values like colors. They can be defined once and reused throughout the app.

Include statementsallow external files (like text files containing variables) to be loaded into apps, ensuring that updates made to the text file are automatically reflected in all apps that use it. This creates a flexible and scalable solution for managing standardization across multiple apps.

Thus, the best way to maintain a standardized color palette across all apps is tocreate all color expressions as variables in a text file and load them into each app using an include statement.

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.

For analyzing the relationship between Sales and Margin, ascatter plotis ideal, as it allows you to visualize the relationship between two measures (Sales and Margin) across various dimensions such as Customer or Product Category. Thefunnel chartis perfect for visualizing stages in a sales process, as it shows how sales progress from the initial stage to the final (Won) stage, with the width of each segment representing the total sales for each stage.

Key Concepts:

Scatter Plot: This type of chart is specifically designed to visualize the correlation or relationship between two measures, making it ideal for analyzing Sales versus Margin across different dimensions.

Funnel Chart: This chart is particularly suited for visualizing the sales stages, as it visually demonstrates the proportion of sales moving through each stage of the sales funnel.

Why the Other Options Are Less Suitable:

A. Scatter plot and Bar chart: While a scatter plot is correct for analyzing Sales and Margin, a bar chart won’t adequately represent the different stages of the sales process as effectively as a funnel chart.

C. Combo chart and Pie chart: A combo chart could potentially work, but it would not show the relationship between Sales and Margin as clearly as a scatter plot. A pie chart is also less effective for representing stages in a sales funnel.

D. Distribution plot and Bar chart: A distribution plot does not effectively show the relationship between two measures, and a bar chart isn’t the best choice for visualizing the stages of a sales process.

References for Qlik Sense Business Analyst:

Scatter Plot for Relationships: This chart type is highly recommended when exploring relationships between two continuous variables, such as Sales and Margin.

Funnel Charts: These are ideal for visualizing how data moves through various stages of a process, such as sales stages, from initial engagement to final sale.

Therefore, the combination of ascatter plotand afunnel chartprovides the best solution, makingBthe correct answer.

Acombo chartis the most suitable visualization to show the value of sales for each month along with a rolling 3-month summary. The combo chart allows you to combine different types of visualizations, such as bars for monthly sales values and a line for the rolling 3-month summary. This provides a clear comparison and tracking of sales trends over time.

Key Concepts:

Rolling Summary: In this case, a 3-month rolling summary can be shown as a line measure in the combo chart, while the sales values for each month can be shown as bars.

Combo Chart: This visualization is ideal for comparing multiple measures on the same axis, such as individual sales values and aggregated rolling summaries.

Why the Other Options Are Less Suitable:

A. Scatter plot: A scatter plot is used to display the relationship between two variables, not to show time-based trends or rolling summaries.

B. Mekko chart: Mekko charts are used for categorical data and comparisons across categories, not for time-based analysis.

D. Pie chart: Pie charts are best suited for showing parts of a whole and are not appropriate for visualizing time-based data or rolling summaries.

References for Qlik Sense Business Analyst:

Combo Charts for Time Series Data: Combo charts are highly recommended when there is a need to compare different types of measures (like individual sales vs. rolling averages) over time in Qlik Sense.

Thus, acombo chartprovides the most effective solution for showing both monthly sales values and the rolling 3-month summary, makingCthe 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 whyOption Bis 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 theSplit functionin 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 thesubfield() functionto 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 theData 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.TheReplace functionwould 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:

TheData Managerprovides powerful tools for transforming and optimizing data before it is used in visualizations. Pre-processing the data using functions likeSplitsignificantly 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 fieldsallow analysts to create new expressions based on transformed data, ensuring that the app remains efficient while meeting analytical needs.

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

To fulfill the CFO’s request for an app that allows each regional finance team to access only their data while navigating from a high-level sheet to detail sheets, the combination of adashboard of KPIsandSection Accessis ideal. A dashboard of KPIs provides high-level insights, andSection Accessensures that users from different regions can only see the data relevant to their region. Section Access allows for controlled access to data, ensuring data security and segregation.

Key Concepts:

Dashboard of KPIs: A dashboard displaying key performance indicators (KPIs) gives a high-level overview of financial data, allowing users to quickly assess critical metrics.

Section Access: This Qlik Sense feature controls data access based on user roles, ensuring that users only have access to the data relevant to their region.

Why the Other Options Are Less Suitable:

B. Pivot table: A pivot table is useful for detailed analysis but not suitable for designing a navigation structure or controlling access to data by region.

C. Multi KPI with set analysis: While set analysis can filter data, it doesn’t control access at the regional level as effectively as Section Access.

D. Dashboard with regional bookmarks: Bookmarks are user-specific and do not offer security or access control, which is required in this scenario.

References for Qlik Sense Business Analyst:

Section Access for Regional Data Control: Qlik Sense recommends Section Access for managing data access when different users need to see only specific subsets of data.

Thus,Ais the best solution because it combines high-level KPIs with robust data access controls using Section Access, making it the correct answer.

To meet the CEO's requirements for a dynamic, interactive, and visually impactful presentation that can also be shared, theStorytellingfeature in Qlik Sense is the best option. TheStorytellingfeature 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,Dis the best choice because it meets all the requirements for interactive and dynamic presentations, making it the verified 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.