Tableau TDS-C01 Practice Test - Questions Answers, Page 31

In Tableau, when you initially add a measure to the rows shelf, you create one mark in the view that represents the aggregate value of that measure. When you then add a dimension with 11 members to the columns shelf, the view is subdivided into 11 separate marks, each representing the measure for one of the dimension members. This results in a total of 11 marks in the view after the dimension is added, one for each member of the dimension.

In the context of Tableau, 'Migrated Data' typically refers to data or workbooks that have been upgraded from a previous version of Tableau Desktop. When you open a workbook in a newer version of Tableau, the data sources within that workbook might be labeled as 'Migrated Data,' indicating that they have undergone a conversion process to be compatible with the new version's features and architecture.

To clear all sorting in a Tableau visualization, you would go to the Worksheet menu, select the 'Clear' option, and then choose 'Sorts.' This action removes all sorting that has been applied to the visualization, including any custom sorting or sorting based on multiple fields. This is a quick way to reset the view to its default sorting state and is particularly useful when you have applied various sorting layers and wish to start fresh.

In Tableau, you can change the font of a worksheet title by double-clicking directly on the title in the view, which opens a dialog box where you can format the text, including changing the font. Another method is to use the Format menu; from there, you select 'Title and Caption,' which opens the Format Title pane on the left side of the screen, where you can change the font and other formatting options for the worksheet title.

The option to create bins in Tableau is available for numeric fields. Bins allow you to group a series of numeric values into larger segments, which can simplify analysis and help in creating histograms or other visualizations that show the distribution of data. For example, you can create bins to group ages into categories like 0-10, 11-20, etc.

With dimension filters in Tableau, you can use several filter modes, including 'Multiple Values (drop-down)' which allows users to select one or more values from a drop-down list of all the dimension's members. Another mode is 'Wildcard Match,' which lets users filter the view by typing in a text box to match dimension members that contain the typed string. These filter types are particularly useful for string or categorical dimensions where users need to search or select specific members to display in the view.

When creating a group by selecting headers in a Tableau view, the newly created grouped dimension replaces the original dimension field on either the Rows or Columns shelf. This grouping action aggregates the selected headers into a single group, and this new group dimension is automatically placed in the view, replacing the original dimension. This functionality allows for more simplified and customized categorization within the data visualization, enhancing the ability to analyze and interpret data according to specific groupings.

Date values in Tableau represent specific points in time and are typically formatted in a standard date format.

Option A, '2020-05-01', is a standard date format representing the 1st of May, 2020.

Option D, 'January 1, 1995', is another example of a date value, representing the 1st of January, 1995. Options B ('December') and C ('Wednesday') represent a month and a day of the week, respectively, but do not specify a particular date.

When dealing with a union of two tables in Tableau, using logical tables with a single table extract is the most appropriate approach. Logical tables allow for the integration of data from multiple tables in a way that is seamless and efficient for analysis. By using a single table extract, Tableau consolidates the data from the union into one extract, optimizing performance and enabling faster data processing. This approach is particularly beneficial when working with large datasets or complex unions.