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From the official documentation:

When you have a view with a large amount of data you might want to explore your data interactively and highlight a specific mark or group of marks while still maintaining the context of where those marks show in your view.

To do this you can turn on the Highlighter for one or more discrete fields that are included in your view and that affect the level of detail

Example - Here we just want to focus on Public Affairs college dimension, but don't want to filter out or remove the rest of the data:

Note that filtering is not the correct option since that would REMOVE the data that doesn't match the filtering criteria.

Reference : https://help.tableau.com/current/pro/desktop/en-us/actions_highlight_highlighter.htm

Explanation

When using a Percent difference, Tableau calculates what the percent change has occured as compared to the last data value. BUT, for the first data value, there is no previous value to compare it to. Hence, it appears as NULL.

Dashboard actions in Tableau are interactive elements that can be used to create a more dynamic experience. These actions can be set to filter data on the dashboard, highlight specific elements, or even direct the user to a URL when interacting with a visualization. They can be triggered by user interaction such as hover, selection, or accessing a context menu. Moreover, dashboard actions can have multiple source sheets (where the action originates) and target sheets (where the action takes effect), allowing for a complex interactivity between different parts of the dashboard.

Treemaps use automatically generated sizes that cannot be resized by using the Marks card. A treemap is a type of visualization that shows hierarchical data as a set of nested rectangles. Each rectangle represents a dimension member and its size is proportional to a measure value. Tableau automatically calculates the size of each rectangle based on an optimal aspect ratio for readability and comparison. You cannot manually adjust the size of each rectangle by using the Marks card or any other option in Tableau10 The other chart types do not use automatically generated sizes that cannot be resized by using the Marks card. Pie charts use angles to show proportions of a whole, not sizes. Histograms use bins and frequencies to show distributions of a measure, not sizes. Area charts use filled areas to show trends over time or categories, not sizes. You can adjust the size of these chart types by using the Marks card or other options in Tableau

When you use the 'Create Set' option in Tableau, it creates a dynamic set if the conditions for inclusion in the set are based on a calculation or an aggregation that may change as the data is updated or as filters are applied. Fixed sets are static and do not change with data refreshes, while dynamic sets can update to reflect the current state of the data.

A histogram is a chart that displays the shape of a distribution. A histogram looks like a bar chart but groups values for a continuous measure into ranges, or bins.

The basic building blocks for a histogram are as follows:

Demo :

When you drop a discrete field on Color in the Marks card, Tableau displays a categorical palette and assigns a color to each value of the field.

When you drop a continuous field on Color, Tableau displays a quantitative legend with a continuous range of colors.

Web version:

Desktop Version:

For more information about color palettes, see Color Palettes and Effects.

According to the Tableau Help, a highlight table is “a cross-tabulation that uses color to encode values”. The help also states that “Highlight tables use the Square mark type” (page 1).

There are three benefits of using an extract as compared to a live connection to a data source:

A live connection to a data source can be slow due to network and user traffic, whereas a connection to an extract improves performance. An extract is a snapshot of data that is stored locally on your computer or on Tableau Server. An extract can reduce the load on the data source and speed up queries.

Extracts are stored in memory (RAM), resulting in faster query performance as compared with live data connections. When you use an extract, Tableau loads the data into memory and optimizes it for analysis. This allows Tableau to perform calculations and aggregations faster than with live connections.

Calculated fields perform better in workbooks connected to extracts than in workbooks with live connections to a data source. Calculated fields are custom fields that you create using formulas or expressions. When you use an extract, Tableau can process calculated fields more efficiently than with live connections.

Explanation

Tableau auto-generates :

1 Dimension - Measure Names

4 Measures - Latitude, Longitude, Number of records, Measure Values

Starting with Tableau 2020.2, every table in a data source has a Count field, in the form of NameofTable(Count). The table count field is an automatically generated, calculated field. (THIS IS NOT PRESENT IN VERSION 2020.1 ON WHICH THE EXAM IS CURRENTLY BASED)