QSBA2024 VALID DUMPS SHEET - QSBA2024 VALID TEST TEST

QSBA2024 Valid Dumps Sheet - QSBA2024 Valid Test Test

QSBA2024 Valid Dumps Sheet - QSBA2024 Valid Test Test

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Qlik Sense Business Analyst Certification Exam - 2024 Sample Questions (Q12-Q17):

NEW QUESTION # 12
The sales manager is investigating the relationship between Sales and Margin to determine if this relationship is linear when choosing the dimension Customer or Product Category.
The sales manager wants to have the potential percentage Sales for each Stage (Initial to Won) of the sales process.
Which visualizations will meet these requirements?

  • A. Scatter plot: Measures X-axis Sales and Y-axis Margin, Dimensions Customer or Product category Bar chart: Dimension Stage, Measure Sales
  • B. Distribution plot: Alternative measures Sales and Margin, Alternatives dimensions Customer or Product category Bar chart: Dimension Stage, Measure Sales
  • C. Scatter plot: Measures X-axis Sales and Y-axis Margin, Alternative dimensions Customer or Product category Funnel chart: Segments Stage, Width Sales
  • D. Combo chart: Measures Sales and Margin, Dimensions Customer or Product category Pie chart: Dimension Stage, Measure Sales

Answer: C

Explanation:
For analyzing the relationship between Sales and Margin, a scatter plot is ideal, as it allows you to visualize the relationship between two measures (Sales and Margin) across various dimensions such as Customer or Product Category. The funnel chart is 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 a scatter plot and a funnel chart provides the best solution, making B the correct answer.


NEW QUESTION # 13
A marketing team needs to display sales for a limited number of products by providing a bar chart that the user can control. The visualization has several requirements:
* Starts with the top five products
* Allows the user to change the number of products displayed
* Allows the user to scroll through all products on a mini chart
The business analyst creates a bar chat and a variable. Which steps should the business analyst complete next?

  • A. * Use the variable to fix the limitation
    * Add a slider object and use the variable to set its value
  • B. * Use the variable to fix the limitation
    * Add an input box to enable the user to enter the required value
  • C. * Add the slider object and use the variable to set its value
    * Use the properties to set the number of bars to custom
  • D. * Add the slider object and use the variable to set its value
    * Set the number of bars to custom and use the variable to set its value

Answer: D

Explanation:
To meet the requirement of controlling the number of products displayed in the bar chart, the business analyst should use a slider object tied to a variable. The variable will store the number of products the user wants to display. In the Appearance section of the bar chart's properties, the analyst can set the number of bars to a custom value using the variable, ensuring that the user can dynamically change the number of displayed products.
Key Concepts:
Slider Object: This provides a user-friendly way for users to adjust the number of products displayed in the bar chart.
Custom Bar Limitation: By setting the number of bars displayed to a custom value controlled by the variable, the business analyst ensures that the user can dynamically adjust how many products are shown.
Why the Other Options Are Less Suitable:
B . Use the variable to fix the limitation and add an input box: While this could work, sliders provide a better, more intuitive user experience than input boxes for adjusting values dynamically.
C . Use the variable to fix the limitation and add a slider: This is almost correct, but it misses the step of setting the number of bars to a custom value using the variable.
D . Add the slider object and set its value, but without custom bar settings: While adding a slider is correct, not setting the number of bars to custom using the variable means the user wouldn't be able to dynamically control the number of displayed products.
References for Qlik Sense Business Analyst:
Dynamic Control with Variables and Sliders: Qlik Sense best practices recommend using sliders and variables to give users control over visualizations, particularly when it comes to dynamically limiting data displayed.
Thus, adding the slider object and setting the number of bars to a custom value controlled by the variable is the best solution, making A the verified answer.


NEW QUESTION # 14
An app needs to load a few hundred rows of data from a .csv text file. The file is the result of a concatenated data dump by multiple divisions across several countries. These divisions use different internal systems and processes, which causes country names to appear differently. For example, the United States of America appears in several places as 'USA', 'U.S.A.', or 'US'.
For the country dimension to work properly in the app, the naming of countries must be standardized in the data model.
Which action should the business analyst complete to address this issue?

  • A. Create a calculated master dimension expression
  • B. Load a lookup table to convert values
  • C. Cleanse the source text file prior to loading
  • D. Use the Replace option in Data manager

Answer: B

Explanation:
In Qlik Sense, when dealing with inconsistent naming conventions across different systems or divisions (like the variation in country names), the best practice is to standardize the data during the loading process. Using a lookup table is the most efficient approach to achieve this. This involves loading a separate table that contains all variations of a country name along with the standardized version. During the load process, Qlik Sense can then map the varying names to a common value.
Key Concepts:
Lookup Table: A lookup table contains key-value pairs where different versions of a data element (like country names) are mapped to a single standard value. In this case, the lookup table could have entries like USA, U.S.A., US all mapped to United States of America.
Data Standardization: This is crucial in ensuring consistent analysis across datasets. By converting variations of country names into a single consistent value, the business analyst ensures that all data visualizations and analysis will treat "USA", "US", etc., as the same entity.
Why the Other Options Are Less Suitable:
A . Create a calculated master dimension expression: While this could theoretically work by creating a calculated expression to handle variations, it's not scalable or maintainable, especially as new variations in country names could appear in future data loads.
C . Cleanse the source text file prior to loading: This option would require modifying the raw data files manually, which is time-consuming and not sustainable if data is frequently updated or if the number of variations is extensive.
D . Use the Replace option in Data manager: The Replace option in the Data Manager could work on a small scale, but it requires manual intervention each time, which is not efficient or sustainable when new data is loaded. Also, it's more useful for one-off corrections than for handling systemic issues across multiple data loads.
References for Qlik Sense Business Analyst:
Data Modeling Best Practices: Lookup tables are a common approach to resolve issues of inconsistent data across multiple sources. They ensure that data is consistently represented in visualizations and reduce the need for manual intervention.
Data Cleansing During Loading: Qlik Sense allows for transformation and data cleansing during the data load process. A lookup table is part of this capability and ensures that the data loaded into the app is clean and consistent.
Using a lookup table is the most scalable and maintainable approach to standardizing country names in this scenario, which is why option B is the verified solution.


NEW QUESTION # 15
Refer to the exhibit.

The users of a Qlik Sense app report slow performance. The app contains approximately 10 million rows of dat a. The business analyst notices the following KPI master measure definition:
Left{ Trim( TransactionName), 1 ) * Right ( TransactionName, 5) Which steps should the business analyst complete to improve app performance?

  • A. Change the master measure definition as follows:
    subfield( TransactionName, '',!)* subfieldl TransactionName, ' ', 3)
  • B. Ask the developer of the underlying database to change the structure of the field TransactionName.
  • C. * 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.
  • D. In the Data manager, use the Replace function to remove the middle part of the field TransactionName

Answer: C

Explanation:
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.


NEW QUESTION # 16
In the 'Sales By Product' bar chart, a customer wants to highlight a specific product bar that includes a dynamic label. The label will only be visible when conditions are met. Which feature should the business analyst add to the bar chart?

  • A. A reference line add-on under Properties
  • B. A Color By Expression property under Appearance
  • C. A Dimension reference line add-on
  • D. An Alternative dimension with a calculation

Answer: B

Explanation:
To dynamically highlight a specific product bar in a bar chart based on conditions, the best approach is to use the Color By Expression feature 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 Appearance
This is the correct answer. The Color By Expression property 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-on
Dimension 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 calculation
Alternative 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 Properties
Reference 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 Expression is 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 use Color By Expression under Appearance.


NEW QUESTION # 17
......

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