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2025年最新のな厳密検証された合格させるQSBA2024試験にはリアル問題と解答
質問 # 19
A business analyst using a shared folder mapped to S:\488957004\ receives an Excel file with more than 100 columns. Many of the columns are duplicates. Any current columns that should be used have the suffix '_c' appended to the column name.
Which action should the business analyst take to load the Excel data?
- A. Open the Excel file, remove all columns that do not have the suffix '_c', and save the file to be loaded
- B. Deselect the fields that do not have the '_c' suffix in the Data manager table preview
- C. Load all columns because the recommended associations will use only columns with the suffix '_c'
- D. Utilize filter functionality in the Data manager to select only columns with the suffix '_c' with a filter condition
正解：B
解説：
When loading data from an Excel file with more than 100 columns, where only columns with the suffix _c are relevant, the most efficient approach is to use the Data Manager. The Data Manager provides a preview of the table being loaded, allowing the business analyst to deselect columns that do not have the _c suffix. This is a quick and straightforward method that avoids manual editing of the Excel file and allows the analyst to focus on the necessary columns.
Key Concepts:
Data Manager Preview: The Data Manager allows you to inspect and modify which columns will be loaded into the data model. The preview panel makes it easy to deselect columns that are not needed.
Efficient Data Loading: By using the Data Manager, the business analyst can avoid loading unnecessary columns, ensuring a cleaner and more manageable data model.
Why the Other Options Are Less Suitable:
A . Load all columns: This would load unnecessary columns, leading to a bloated data model with duplicates and irrelevant data.
B . Utilize filter functionality: While filtering could work, deselecting fields directly in the preview is more efficient and straightforward.
C . Edit the Excel file: Manually editing the Excel file is unnecessary and could lead to errors, especially when Qlik Sense provides tools to handle this within the platform.
References for Qlik Sense Business Analyst:
Data Manager for Field Selection: Qlik Sense recommends using the Data Manager to inspect and selectively load data fields, which is particularly useful when dealing with large datasets.
Thus, D is the best solution because it allows for selective loading of relevant columns, making it the correct answer.
質問 # 20
The business analyst creates one table by concatenating and joining several source tables. This has resulted in a table of several thousand rows that may have several columns containing between 30% and 70% null values. The business analyst needs to understand the level of null values in each field of this table to determine if this is an issue.
Which capability should the business analyst use?
- A. Look at the tags fields for any indication that $null is associated to this field
- B. Select each field in the Data model viewer and use the Density value to determine the level of nulls
- C. Inspect each field in the Data model viewer and use the Subset ratio to determine the level of null values
- D. Enable the Preview Panel in the Data model viewer and inspect the data table visually to determine the level of null values
正解：B
解説：
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.
質問 # 21
The VP of Sales asks a business analyst to include a KPI object on the sales dashboard that shows total sales value for the year 2022, regardless of selections. Existing fields in the data model include Sales and Year.
How should the business analyst write the measure for the KPI object?
- A. Sum( { 1 < year="{" '2022'="" )="M /> ) Sales)
- B. Sum( { $ < year="{" '2022*="" )="" /> } Sales)
- C. Sum( 1 { < year="(" '2022'="" )="" /> ) Sales)
- D. Sum( { < year="|" '2022,="" }="" /> } Sales)
正解：A
解説：
In this scenario, the VP of Sales wants to see the total sales for the year 2022 regardless of selections. This requirement is best handled using Set Analysis in 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, is C.
質問 # 22
A business analyst is developing an app that requires a complex visualization. The visualization is very similar in style and configuration to another visualization in a different app, but the data models are completely different.
Which action should the business analyst take to most efficiently create the new visualization?
- A. Add the base visualization to the master items and use it as a template for the new visualization.
- B. Open both apps at the same time. Drag the base visualization between apps, then update the data properties.
- C. Copy and paste the visualization between the apps, and update the data properties in the new app.
- D. Note the properties of the base visualization and create the new visualization from scratch.
正解：C
解説：
When working with Qlik Sense apps, a business analyst often encounters situations where visualizations may be highly similar between different apps, even if the underlying data models differ. In such cases, efficiency is crucial, and Qlik Sense provides several methods to reuse visualizations across apps. Let's break down the options:
A . Add the base visualization to the master items and use it as a template for the new visualization.
This option suggests adding the base visualization to the master items. While master items are useful for reusing dimensions, measures, and visualizations within the same app, they do not easily transfer across apps. In this case, since the visualization is required in a different app, this approach would not be the most efficient or feasible.
B . Note the properties of the base visualization and create the new visualization from scratch.
This option involves manually noting the properties and then replicating them in the new app. While this would work, it is labor-intensive and increases the likelihood of human error, especially in complex visualizations. It is not an efficient solution for business analysts looking to save time.
C . Copy and paste the visualization between the apps, and update the data properties in the new app.
This is the most efficient solution. Qlik Sense allows for the copying and pasting of visualizations between different apps, and you can then adjust the properties to fit the new data model. This option enables the business analyst to leverage existing visual work without having to recreate it from scratch. Updating the data properties, such as dimensions and measures, ensures that the visualization functions correctly with the new data model.
D . Open both apps at the same time. Drag the base visualization between apps, then update the data properties.
While this seems like a practical option, Qlik Sense does not allow users to drag and drop visualizations directly between different apps. As a result, this method is not possible.
Key Qlik Sense Business Analyst References:
Copying and pasting visualizations is a common practice in Qlik Sense when working between different apps. The ability to quickly replicate and adapt visualizations across apps helps streamline the development process.
Adjusting data properties such as dimensions and measures ensures that visualizations adapt to different data models without the need for full recreation.
Efficiency and error reduction are critical in app development, and copy-paste functionalities are specifically designed to reduce manual work in such scenarios.
In conclusion, the correct and most efficient action for the business analyst to take is C, copy and paste the visualization, and then update the relevant data properties.
質問 # 23
A business analyst is creating an app that contains a bar chart showing the top-selling product categories. The users must be able to control the number of product categories shown.
Which action should the business analyst take?
- A. Use a rankQ function in the sales expression
- B. Use firstsortedvalue() function to extract the required product categories
- C. Create a variable and variable input object and use variable in dimension limit field
- D. Create a variable and variable input object and use variable in the sales expression
正解：C
解説：
When users need control over how many product categories are shown in a bar chart, the most effective solution is to use a variable input object. This allows users to dynamically adjust the number of categories displayed.
A: Create a variable and variable input object and use the variable in the dimension limit field.
This is the correct solution. By creating a variable and using the Variable Input object, the user can dynamically control the number of product categories shown in the bar chart by adjusting the dimension limit. This method provides flexibility and an intuitive interface for the user.
B: Use firstsortedvalue() function to extract the required product categories.
The firstsortedvalue() function is typically used to extract the first occurrence of a value based on sorting criteria, but it's not the best approach for controlling the number of displayed categories dynamically.
C: Create a variable and variable input object and use the variable in the sales expression.
While variables can be used in expressions, this approach is less efficient than using the dimension limit field, which is specifically designed for controlling the number of displayed values.
D: Use a rankQ function in the sales expression.
The rankQ function ranks data, but it's not the most efficient or intuitive method for dynamically controlling the number of product categories displayed in a bar chart. It would require more complex expressions compared to the straightforward use of a variable in the dimension limit field.
Key Qlik Sense Business Analyst References:
The Variable Input object allows users to interact with and adjust variables within the app. This is ideal for giving users control over visual elements like the number of categories displayed in a chart.
The Dimension Limit field is specifically designed to control how many items (like product categories) are shown in a chart based on a ranking or expression.
Thus, the best approach to allow users to control the number of product categories displayed is to create a variable and variable input object, and use the variable in the dimension limit field.
質問 # 24
A business analyst is building an app to analyze virus outbreaks. They create a bar chart using a dimension of Continent, and a measure of Sum (Knowning sections). They require a secondary bar on the chart, so they create a second measure using Count (MajorCities).
The bar chart adjusts, but no bars are visible for this second measure. Which action should the business analyst take to resolve this issue?
- A. Convert the bar chart to a combo chart and reconfigure the second measure to be a bar
- B. Enable Value labels within the Presentation section of the Appearance properties
- C. Recreate the second measure as an alternative measure
- D. Change the Y-axis Range scale from Auto to Custom and select a suitable Max value
正解：A
解説：
In this scenario, the second measure (Count of MajorCities) is likely not being displayed because the two measures-Sum(Knowing sections) and Count(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 a combo 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 for Sum(Knowing sections)) and another axis for the second measure (e.g., bars for Count(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 a combo chart ensures both measures are properly displayed, making D the correct answer.
質問 # 25
A business analyst is creating an app for the sales team. The app must meet several requirements:
* Compare 10 top-performing sales representatives and the amount of sales for each
* Investigate margin percentage and total sales by each product category
* View distribution of sales amount
Which visualizations should be used for this app?
- A. A treemap, container, and distribution plot
- B. A bar chart, line chart, and scatter plot
- C. A treemap, box plot, and histogram
- D. A bar chart, scatter plot, and histogram
正解：D
解説：
For this scenario, using a bar chart, scatter plot, and histogram provides the best coverage of the requirements. The bar chart is ideal for comparing the sales performance of the top 10 sales representatives. The scatter plot is used to analyze the relationship between margin percentage and total sales by product category. The histogram is excellent for visualizing the distribution of sales amounts.
Key Concepts:
Bar Chart: Perfect for comparing categorical data, such as sales amounts across different sales representatives.
Scatter Plot: Ideal for exploring relationships between two variables, such as margin percentage and total sales.
Histogram: Provides a clear visualization of the distribution of a continuous variable, such as sales amounts.
Why the Other Options Are Less Suitable:
B . Treemap, Container, and Distribution plot: A treemap is less effective for comparing individual sales reps, and a container does not provide a clear visualization on its own.
C . Bar chart, Line chart, and Scatter plot: A line chart is not needed in this case, as it is best for showing trends over time, which is not required here.
D . Treemap, Box plot, and Histogram: A box plot is more suited for showing statistical distributions (e.g., quartiles), which is unnecessary in this case.
References for Qlik Sense Business Analyst:
Data Exploration: Bar charts, scatter plots, and histograms are among the most commonly recommended visualizations for comparing performance, analyzing relationships, and viewing distributions in Qlik Sense.
Thus, the combination of a bar chart, scatter plot, and histogram offers the most comprehensive solution, making A the correct answer.
質問 # 26
A business analyst needs to create a visualization that compares two measures over time using a continuous scale that includes a range. The measures will be Profit and Revenue.
Which visualization should the business analyst use?
- A. Bar chart
- B. Bullet chart
- C. Scatter plot
- D. Line chart
正解：D
解説：
A line chart is 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, the line chart is the best choice for this scenario, making A the correct answer.
質問 # 27
A business analyst is creating a data model from several Excel files that contain several hundred thousand rows of dat a. The requirements include:
* Users need to drill down to various details within the available data set.
* Several final measures will be repeatedly used. These final measures require combining several fields in a single table.
* User experience and load time is a high priority.
Which action should the business analyst take to meet these requirements?
- A. Combine the various source fields in a calculated item in the Data manager
- B. Combine the source fields and create additional fields in Excel
- C. Develop a master item using the required source fields
- D. Aggregate the data to the source period
正解：C
解説：
In Qlik Sense, creating Master Items allows 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, making B the best and verified option for this scenario.
質問 # 28
Two customers in an organization want to use an app that contains a finance data set. With different analysis objectives, each customer will only use a subset of that data. Which procedure should the business analyst follow?
- A. Apply Section Access to manage the data for each customer
- B. Unpivot, then re-associate the data tables for each customer
- C. Duplicate and rename the apps for each customer
- D. Create multiple visualizations using set analysis
正解：D
解説：
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.
質問 # 29
A customer needs to demonstrate the value of sales for each month of the year with a rolling 3-month summary. Which visualization should the business analyst recommend to meet the customer's needs?
- A. Combo chart
- B. Mekko chart
- C. Pie chart
- D. Scatter plot
正解：A
解説：
A combo chart is 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, a combo chart provides the most effective solution for showing both monthly sales values and the rolling 3-month summary, making C the correct answer.
質問 # 30
A business analyst needs to rapidly prototype an application design for a prospective customer. The only dataset provided by the customer contains 30 fields, but has over one billion rows. It will take too long to keep loading in its entirety while the analyst develops the data model.
Which action should the business analyst complete in the Data manager?
- A. Use the Filter data option to reduce the number of rows
- B. Split the dataset and create a normalized star schema of associated tables
- C. Deselect text columns with unique data values to reduce the memory footprint
- D. Truncate text fields longer than 256 characters to create preview fields
正解：A
解説：
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 a Filter data option 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 the Filter data option allows the business analyst to focus on a smaller subset of data, enabling quicker prototyping and iteration, which makes option C the most effective solution.
質問 # 31
A customer needs to distribute sales data to a variety of teams. The internal analyst team requires a global view of dat a. The sales team requires mobile device access.
Which solution will meet the needs of both teams?
- A. One app with various objects
- B. One app with a specific extension for mobile users
- C. Two apps: one designed for mobile and one for internal use
- D. A mashup with various objects
正解：C
解説：
To meet the needs of both the internal analyst team and the sales team, the best solution is to create two separate apps: one designed specifically for mobile use and another for internal use. Mobile devices require different UI considerations, such as simpler, touch-optimized layouts, while the internal team can benefit from a more detailed app optimized for desktop use. Designing separate apps ensures that both teams have a tailored experience that suits their specific devices and use cases.
Key Concepts:
Mobile Optimization: Mobile devices require apps that are streamlined and optimized for smaller screens, while internal users on desktop computers can handle more complex layouts and detailed reports.
Separate Apps: Creating separate apps ensures that each team gets the best user experience tailored to their needs.
Why the Other Options Are Less Suitable:
A . One app with a specific extension for mobile users: While extensions can provide some mobile functionality, they don't offer the flexibility and optimization needed for a fully mobile-friendly experience.
C . A mashup with various objects: A mashup may provide flexibility, but it could be overly complex for this requirement and wouldn't necessarily offer an optimal mobile experience.
D . One app with various objects: This could complicate the user experience for both teams, as mobile users may struggle with objects that are not optimized for their devices.
References for Qlik Sense Business Analyst:
Mobile vs. Desktop App Design: Qlik Sense recommends optimizing apps for specific devices to ensure the best user experience for both desktop and mobile users.
Thus, B is the correct answer because it provides the best solution for both the mobile sales team and the internal analyst team, making it the verified answer.
質問 # 32
A business analyst needs to build a chart that enables users to analyze the correlation between the following measures for all products:
* Product Sales ($)
* Order Volume
* Margin%
Which visualization should the business analyst use?
- A. Pivot table
- B. Scatter plot
- C. Combo chart
- D. Multi KPI
正解：B
解説：
A scatter plot is the most appropriate visualization for analyzing the correlation between Product Sales ($), Order Volume, and Margin %. 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, the scatter plot is the most effective visualization for analyzing the correlation between Product Sales, Order Volume, and Margin %, making C the correct answer.
質問 # 33
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. Use the Replace option in Data manager
- C. Load a lookup table to convert values
- D. Cleanse the source text file prior to loading
正解：C
解説：
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.
質問 # 34
A business analyst receives multiple requests for a variety of different filter panes to be placed on a dashboard. Users need to filter on many different values across different fields.
Which Qlik Sense feature do the users need to learn about to meet their needs?
- A. Insight Advisor
- B. Smart search
- C. Data model viewer
- D. Governed self-service
正解：B
解説：
When users need to filter across many different fields and values in a Qlik Sense dashboard, the most efficient feature they can use is Smart Search. Smart Search allows users to quickly search across all fields within the data model, enabling them to find relevant information and apply filters in a streamlined manner.
A . Smart search
This is the correct option. Smart Search enables users to enter search terms and find matches across all fields in the data model, allowing for quick and intuitive filtering. It helps users locate specific data points or filter across multiple fields at once, making it highly efficient when multiple filter panes are needed.
B . Data model viewer
The Data Model Viewer provides a visual representation of the relationships between data tables in the model. While it's useful for understanding the data structure, it's not a tool for filtering or user interaction with data.
C . Insight Advisor
The Insight Advisor is designed for guided analytics, providing suggestions and generating visualizations based on user queries. It does not offer the comprehensive filtering capabilities that Smart Search does.
D . Governed self-service
Governed self-service refers to the balance between providing users with flexibility in creating their own visualizations while maintaining control over data governance. It's not related to filtering or searching data in the same way as Smart Search.
Key Qlik Sense Business Analyst References:
Smart Search in Qlik Sense is designed to provide fast, interactive search capabilities that span across all fields, enabling complex filtering in an easy-to-use interface.
This feature allows users to filter multiple fields simultaneously, saving time and effort when analyzing diverse data sets.
Thus, the correct feature for filtering on multiple values across different fields is Smart Search.
質問 # 35
A business analyst is creating an app using a dataset from ServiceNow. The dataset shows information about support cases, including how many days it has been since the case was opened (age).
The app requirements are:
* The dashboard must display support cases in categories based on the age (New, Aging, and Beyond Service Level Agreement)
* The categories will be used multiple times in the dashboard
* Given the volume of support cases, it is expected that the dataset will grow to be very large Which solution is the most efficient way for the business analyst to create this app?
- A. Create an Excel sheet with all possible age values and the corresponding categories to add to the data model
- B. Ask the ServiceNow team to create the field in the source dataset
- C. Write a master dimension with a nested IF statement to group ages together
- D. Create a new field for the categories using the Bucket option in the Data manager
正解：D
解説：
To efficiently categorize support cases based on age (New, Aging, Beyond SLA) for use in multiple places across the dashboard, the Bucket option in the Data Manager is 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 the Bucket option to create a new field for categories is the most efficient solution, making C the correct answer.
質問 # 36
A company CFO has requested an app that contains visualizations applicable to analyzing the finance dat a. Each regional finance team will analyze their data and should only have access to the data in their region. The app must contain a high-level sheet that navigates to relevant detail sheets.
Which features support a logical design structure?
- A. A dashboard with regional bookmarks
- B. A Multi KPI with set analysis
- C. A pivot table that filters by region
- D. A dashboard of KPIs and section access
正解：D
解説：
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 a dashboard of KPIs and Section Access is ideal. A dashboard of KPIs provides high-level insights, and Section Access ensures 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, A is the best solution because it combines high-level KPIs with robust data access controls using Section Access, making it the correct answer.
質問 # 37
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