QSBA2024 Exam Dumps : Qlik Sense Business Analyst Certification Exam-2024

The banking dashboard requires several KPIs and visualizations based on current year (CY) and last year (LY) revenue, as well as cost and customer satisfaction. To build the required measures, the fieldsCY Revenue, LY Revenue, and CY Costare necessary because they provide the comparison between the current and previous year, as well as cost-related data for the specific calculations.

Key Concepts:

KPI for CY vs LY Revenue: This requires both CY Revenue and LY Revenue fields.

Scatter Plot for Cost vs Revenue: This comparison requires both CY Revenue and CY Cost to create the necessary relationship in the scatter plot.

Treemap for Customer Satisfaction: In addition to the customer satisfaction rating, the field CY Revenue would likely be used for additional insights by region.

Table with CY Cost: The table requires CY Cost data along with other relevant fields like record number, financial instrument, and bank.

Why the Other Options Are Less Suitable:

B. CY Revenue, LY Revenue, Cost: While this covers the revenue fields, it does not specifically includeCY Cost, which is needed for the table and scatter plot.

C. Revenue and Cost: This option is too general and does not distinguish between current year and last year revenue, which is essential for the KPI.

D. Revenue and CY Cost: This does not include LY Revenue, which is required for the KPI.

References for Qlik Sense Business Analyst:

Comparison Measures in Qlik Sense: In Qlik Sense, comparisons across time periods like CY and LY require specific fields to calculate accurate insights, and it’s essential to ensure the appropriate fields are included for each visualization.

Thus,Aprovides the correct set of fields for the required dashboard, making it the verified answer.

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.

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.

ThePersistent colorsandBy Dimensionsettings offer a straightforward and maintainable way to create color associations across charts, making optionCthe verified solution.