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A business analyst needs to create two side-by-side charts for a sales department with the following data:
* Number of orders
* Name of the customer
* Percentage of margin
* Total sales
The charts use a common dimension, but each chart has different measures. The analyst needs to create a color association between the two charts on the dimension values.
Which action should the business analyst take?
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.
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 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.
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?
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.
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?
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.
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?
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.
