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?
For a new report, a consultant needs to build a data model with three different tables, including two that contain hierarchies of locations and products. The third
table contains detailed warehousing data from all locations across six countries. The consultant uses Tableau Cloud and the size of the third table excludes
using an extract.
What is the most performant approach to model the data for a live connection?
For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau's relationships feature, which optimizes queries for the underlying database.
A university has data on its undergraduate students and their majors by grade level (Freshman, Sophomore, Junior, Senior). The university is interested in
visualizing the path students take as they change majors across grade levels.
Which visualization type should the consultant recommend?
To visualize the path students take as they change majors across different grade levels, a Sankey Diagram is highly effective. This type of visualization illustrates the flow and quantity between different stages or categories:
Sankey Diagram: It allows for a visual representation of students' movements between majors over time. Each flow's thickness is proportional to the number of students moving from one major to another, giving a clear, immediate visual cue of major popularity and student migration patterns.
To create a Sankey Diagram in Tableau, you typically need to prepare the data specifically for this type of chart. The data must include source (starting major), target (ending major), and the value (number of students). It often requires custom calculations and data reshaping to get the data in a format that a Sankey can use.
Once the data is prepared, you can use a combination of calculated fields, path binning, and line charts to simulate the flow effect in Tableau. External plugins or web-based integrations might also be employed for more direct implementations.
Reference Sankey Diagrams are not natively supported in Tableau but can be implemented through creative use of data preparation and calculations, as suggested in advanced Tableau user communities and demonstrated in various Tableau public galleries.
A consultant plans a Tableau deployment for a client that uses Salesforce. The client wants users to automatically see Tableau views of regional sales filtered
by customer as soon as the users sign into Salesforce.
Which approach should the consultant use to deliver the final visualization?
To ensure that users automatically see Tableau views of regional sales filtered by customer as they sign into Salesforce, embedding the views directly into Salesforce is most effective:
Embedding Views: Tableau provides capabilities to embed its dashboards into web applications such as Salesforce. This approach ensures that the visualization is part of the Salesforce user interface, enhancing user experience by not requiring users to navigate away from Salesforce to view the data.
Implement this by using Tableau's embedding code, which can be generated from the Tableau Server for each specific view. Place this embed code into the Salesforce Visualforce pages or use Salesforce Canvas to integrate these views seamlessly.
This setup allows the Tableau views to inherit user credentials from Salesforce, enabling personalized data visualization based on the user's access rights and region, directly aligned with their Salesforce login session.
Reference The embedding technique is documented in both Tableau's and Salesforce's official integration guides, which provide step-by-step instructions on embedding Tableau views into Salesforce platforms.
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?
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.
Reference 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.
Elsa
1 days agoFranklyn
3 days agoBuddy
16 days agoLindy
1 months agoJacqueline
1 months agoHoward
1 months ago