QSBA2024 Online Practice Questions

Explanation:
To meet the requirement of dynamically changing the number of countries and cities displayed in the visualizations, the business analyst should use variables to control the number of countries and cities shown. By creating two variables (vCountry and vCity), the analyst can use sliders to allow the user to adjust the maximum number of countries (up to 10) and cities (up to 5 per country) that are displayed. These variables can then be applied to each visualization to control the displayed data.
Key Concepts:
Variables: Variables allow users to dynamically adjust values in Qlik Sense visualizations. In this case, sliders can be used to change the number of countries and cities displayed.
Slider Object: Adding a slider enables users to easily adjust the values of the variables vCountry and vCity in a user-friendly manner.
Why the Other Options Are Less Suitable:
B. Input field and dimension limitation: This option is unnecessarily complex and doesn't provide the same dynamic control as using variables and sliders.
C. Input field without sliders: While input fields could work, sliders offer a more intuitive way for users to adjust the values dynamically.
D. Sheet action: Setting a sheet action to limit the data displayed is less flexible and doesn't provide the same dynamic interaction as using variables and sliders.
References for Qlik Sense Business Analyst:
Dynamic Visualizations with Variables: Qlik Sense recommends using variables and interactive objects like sliders to give users control over dynamic data visualizations.
Thus, the most effective solution is to create variables and use sliders to dynamically control the number of countries and cities, making A the correct answer.

Explanation:
A box plot is 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, the box plot is the best choice for visualizing salary distribution with standard deviation indicators, making B the verified answer.

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.

Explanation:
The Density value in the Data Model Viewer provides a measure of how "dense" or "sparse" a field is in terms of data completeness. A higher density value means fewer nulls, while a lower value indicates more nulls. By checking the density value for each field, the business analyst can determine the percentage of non-null values, which is critical for understanding data quality and completeness. Key Concepts:
Density Value: This is a measure in Qlik Sense that indicates the proportion of non-null values in a field. A field with a high density is mostly populated, while a lower density indicates a high proportion of null values.
Data Model Viewer: This tool allows analysts to inspect the structure and quality of data fields, including metrics such as density.
Why the Other Options Are Less Suitable:
B. Preview Panel: While the Preview Panel shows sample data, it does not provide a comprehensive measure of null values and is more suited for a quick glance rather than detailed analysis.
C. Tags fields with $null: This would show if the field contains any nulls, but it wouldn’t quantify the level of nulls.
D. Subset Ratio: The subset ratio compares values across related tables, not null values within individual fields.
References for Qlik Sense Business Analyst:
Data Quality in Qlik Sense: Using the Density value is the best way to assess the proportion of null values in a field, making it ideal for the business analyst to understand the completeness of the data. Thus, A is the correct answer because the density value provides the required insight into the level of nulls in each field.

Explanation:
A scatter plot is 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 using K-Means clustering to color the products, the business analyst can visually group similar products.
Additionally, adding a median sales volume reference line makes 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 a scatter plot with K-Means clustering and a median sales volume reference line is the best approach, making D the verified answer.