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NEW QUESTION # 25
A business analyst is creating a view of the top 10 customers for each region. The analyst has set a "Top 10" filter on Customer Name. However, it did not display the top 10 customers per region, as shown in the image below.
Which type of filter should the business analyst add to filter for region?
Answer: A
Explanation:
The issue occurs because of Tableau's Order of Operations.
Key Tableau logic:
* Top N filters are a type of Dimension filter.
* Dimension filters are evaluated after Context filters.
* When you place Region on Filters (as a standard dimension filter), Tableau:
* First applies the Customer Name Top 10 filter across the entire data set, not per region.
* Then limits the view to the selected region(s).
* This results in seeing the global Top 10 customers, not the Top 10 per region.
How to fix it:
To force Tableau to compute Top 10 customers within each region, the Region filter must be applied before the Top N Customer filter.
This is done by making Region a Context Filter.
Effect of a Context Filter:
* Context filters are executed before the Top N filter.
* Region becomes the context.
* Tableau then evaluates the Top 10 customers inside each region's subset of data.
This produces the correct "Top 10 customers per region".
Why the other options are incorrect:
A). Extract filter
Applies once when creating the extract; does not control Top N logic inside the workbook.
B). Dimension filter
This is what the analyst already has - and it causes the unwanted behavior because it happens after the Top N filter.
C). Table Calculation filter
Top N is not a table calculation; table calc filters cannot fix this problem.
Only the Context Filter changes the execution order so Top N works per region.
* Tableau Order of Operations showing Context Filters applied before Top N filters.
* Best practices recommending Context Filters when Top N must be computed within subcategories.
* Filtering documentation explaining that Top N filters require context when additional dimensional filters are present.
NEW QUESTION # 26
A consultant is tasked with improving the performance of a large workbook that contains multiple dashboards, each of which leverages a separate data source. What is one way to improve performance?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's performance best-practice documentation explains that large workbooks containing many dashboards, multiple data sources, and complex interactions have heavier memory and CPU requirements. When a workbook grows too large, Tableau must:
* Load every data source
* Cache metadata for all sheets
* Maintain connections across all dashboards
* Render more worksheets simultaneously
This increases workbook load time and slows dashboard performance.
A documented method for improving performance is to split a large workbook into multiple smaller workbooks, each containing only the dashboards relevant to a particular audience or subject area. Smaller workbooks:
* Reduce the amount of metadata Tableau must load
* Reduce extract sizes per workbook
* Improve caching efficiency
* Improve dashboard loading and rendering time
Options A, B, and C do not improve workbook performance:
* A. Converting Data Source Filters to Quick Filters reduces performance because Quick Filters (interactive filters) are more expensive than Data Source filters and slow down rendering.
* B. Converting extracts to live connections typically worsens performance because live sources depend on database query speed, network latency, and server load.
* C. Restricting users does not change workbook performance, only access.
Splitting the workbook is a recognized Tableau strategy for improving performance of large, multi-dashboard workbooks.
* Tableau Performance Optimization guidelines encouraging splitting large workbooks into smaller ones.
* Workbook design best practices emphasizing reduced complexity and smaller metadata footprints.
* Documentation on performance costs associated with Quick Filters and multiple data sources.
NEW QUESTION # 27
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?
Answer: C
Explanation:
To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:
Correct Formula: { FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } This calculation uses a Level of Detail (LOD) expression:
The left part of the formula { FIXED [Order ID] : SUM([Sales]) } calculates the total sales for each individual order.
The right part { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } calculates the average sales per order within each region.
The > operator is used to compare these two values to determine if the sales for each order exceed the regional average.
References
This formula utilizes Tableau's LOD expressions to perform complex comparisons across different dimensions of the data, as explained in Tableau's official training materials on LOD calculations.
NEW QUESTION # 28
A client's dashboard has two sections dedicated to their shops and warehouses shown when a viewer chooses either shops or warehouses with a parameter.
There are a few quick filters that apply to both, while others apply to only shops or only warehouses.
Currently, the quick filters are all shown at the left side of the dashboard. The client wants to hide all filters, but when shown, make it easy for the viewer to find the quick filters that work for only shops or only warehouses.
Which solution should the consultant recommend that meets the client's needs and is most user-friendly?
Answer: B
Explanation:
The most user-friendly solution is to use Dynamic Zone Visibility in combination with a Show/Hide Button.
This approach allows the dashboard to dynamically display only the relevant quick filters based on the viewer' s selection of shops or warehouses, thus reducing clutter and focusing the user's attention on applicable filters.
The Show/Hide Button further enhances the user experience by allowing viewers to toggle the visibility of the filter container, providing a clean and organized dashboard interface1.
References: Dynamic Zone Visibility is a feature in Tableau that enables dashboard elements to appear or disappear based on the value of a field or parameter1. This functionality is ideal for creating interactive and user-friendly dashboards that adapt to user interactions and selections1.
NEW QUESTION # 29
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?
Answer: C
Explanation:
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References: The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts. These practices suggest keeping the extract's data set short through filtering1. Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
Extract Filter: This involves setting filters that apply directly when the data is extracted from the source.
This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.
NEW QUESTION # 30
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