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NEW QUESTION # 51
The human resources department needs to see a distribution of salaries broken down by department with standard deviation indicators.
Which visualization should the developer use?
Answer: C
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.
NEW QUESTION # 52
A company has recently implemented Qlik Sense. A user is looking to use natural language questions to help create content. Which feature can achieve this goal?
Answer: D
Explanation:
In Qlik Sense, the Insights Advisor Chat is the feature that allows users to interact with the app through natural language questions. This tool leverages Qlik's advanced AI and machine learning capabilities to interpret natural language queries and generate relevant insights, visualizations, or suggestions for analysis.
A . Advanced Authoring
Advanced Authoring is a set of tools in Qlik Sense designed for creating detailed visualizations and reports, but it does not include natural language interaction capabilities. It is focused more on customization and precise design rather than conversational querying.
B . Story and Bookmarks
Storytelling and bookmarks in Qlik Sense are tools for narrative data presentations and saving specific states of analysis. They do not provide the ability to ask natural language questions or automatically generate insights.
C . Insights Advisor Chat
Insights Advisor Chat is the correct answer. This feature allows users to interact with their data by typing natural language questions, which the system interprets to generate appropriate responses, including charts, KPIs, and other insights. It is designed to assist non-technical users by making data exploration more intuitive and accessible through natural language.
D . Associative Engine
The Associative Engine is the underlying technology that allows Qlik Sense to handle large datasets and perform associative searches across them. While it is powerful for data exploration, it does not provide a direct interface for natural language querying like Insights Advisor Chat does.
Key Qlik Sense Business Analyst References:
Insights Advisor Chat is a feature in Qlik Sense that empowers users to ask questions in natural language and get meaningful responses without needing to be data experts.
It is part of Qlik Sense's broader set of augmented intelligence tools that enhance the user experience by providing guided insights and helping users discover relationships in data through natural language queries.
This feature simplifies data exploration for business users who might not be familiar with complex data querying techniques.
Thus, the feature that allows users to use natural language questions in Qlik Sense is Insights Advisor Chat.
NEW QUESTION # 53
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?
Answer: D
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 # 54
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?
Answer: D
Explanation:
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.
NEW QUESTION # 55
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?
Answer: B
Explanation:
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.
NEW QUESTION # 56
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