We can perform filter, URL and highlight actions out of the above given choices on a dashboard. Please refer to the image below:

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

This is true, indeed!

The LEFT JOIN keyword returns all records from the left table (table1), and the matched records from the right table (table2). The result is NULL from the right side, if there is no match.

Reference: https://www.w3schools.com/sql/sql_join_left.asp

THIS IS A VERY IMPORTANT QUESTION

Aggregation is an important concept to consider when creating calculated fields. A calculated field for SUM([Profit]) / SUM([Sales]) will give you a very different answer than [Profit] / [Sales], even though both formulas are valid.

If you do not provide the aggregation within the calculated field, Tableau will calculate the equation for every record ( row ) in your analysis, then aggregate the answers for all of the rows together when the calculated field is added to the view.

In simple terms, if specify the aggregation such as SUM, what Tableau will do is that it will first calculate the sum of the Profit column ( say x ), then calculate the sum of the Sales column ( say y ), and then simply apply x/y ---> This is what we expect! Perfect!

BUT, if you don't specify the aggregation, it will go to every single ROW, perform Profit / Sales, and then aggregate the answers calculated for each row. This is simply NOT what we wan't!

An example:

Reference: https://www.linkedin.com/pulse/tableau-tip-dont-make-error-ratio-calculations-bob-newstadt

A Pareto chart is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the ascending cumulative total is represented by the line.

On the primary axis, bars are used to show the raw quantities for each dimension member, sorted in descending order.

On the secondary axis, a line graph is used to show the cumulative total in percent format.

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

For relational data sources, dates and times are automatically placed in the Dimensions area of the Data pane and are identified by the date or date-time icon. For example, the Order Date and Ship Date dimensions from an Excel data source are shown below:

When you place a relational date on a shelf, the field name is automatically modified to reflect the default date level. Tableau defines the default date level to be the level at which there are multiple instances. For example, if the date field includes multiple years, the default level is year. However, if the date field contains data for just one year but includes multiple months, then the default level is month.

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

Tableau Data Extract (TDE) is a snapshot of the data that Tableau stores locally. Good for very large datasets of which we only need few fields. Performance is optimised because it queries its own database engine instead of the local data source.

When you create an extract of your data, you can reduce the total amount of data by using filters and configuring other limits. After you create an extract, you can refresh it with data from the original data. When refreshing the data, you have the option to either do a full refresh, which replaces all of the contents in the extract, or you can do an incremental refresh, which only adds rows that are new since the previous refresh.

Extracts are advantageous for several reasons:

1) Supports large data sets: You can create extracts that contain billions of rows of data.

2) Fast to create: If you're working with large data sets, creating and working with extracts can be faster than working with the original data.

3) Help improve performance: When you interact with views that use extract data sources, you generally experience better performance than when interacting with views based on connections to the original data.

4) Support additional functionality: Extracts allow you to take advantage of Tableau functionality that's not available or supported by the original data, such as the ability to compute Count Distinct.

5) Provide offline access to your data: Extracts allow you to save and work with the data locally when the original data is not available. For example, when you are traveling.

It contains all the measures in your data, collected into a single field with continuous values - This is the definition for 'Measure Values'.

All others are True w.r.t. Measure Names!

The Measure Names field contains the names of all measures in your data, collected into a single field with discrete values.

Documentation : https://help.tableau.com/current/pro/desktop/en-us/datafields_understanddatawindow_meavalues.htm

Cluster analysis partitions marks in the view into clusters, where the marks within each cluster are more similar to one another than they are to marks in other clusters.

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

Joins are used to combine data or rows from two or more tables based on a common field between them. This can cause data loss or duplication if tables are at different levels of detail, and joined data sources must be fixed before analysis can begin.

Inner join   

Left Join

While joins can filter data, relationships always preserve all measures. Now important values like money can never go missing. And unlike joins, relationships won’t double your trouble by duplicating data stored at different levels of detail.

Reference: https://www.tableau.com/about/blog/2020/5/relationships-part-1-meet-new-tableau-data-model

Replace References:

When you successfully connect to a new data source, all worksheets in the workbook that previously referred to the original data source now refer to the new data source. If the new data source does not have the same field names as the original workbook, the fields become invalid and are marked with an exclamation point . You can quickly resolve the problem by replacing the field’s references.

For example, say you have a workbook connected to a data source that contains a Customer Name field. Then you edit the data source to point to a new data source that has all the same data but instead of Customer Name, the field name has been changed to Name. The Customer Name field remains in the Data pane but is marked as invalid. To make the field valid, you can replace the references, which means you can map the invalid field to a valid field in the new data source (for example, Customer Name corresponds to Name).

Read more at : https://help.tableau.com/current/pro/desktop/en-us/howto_connect.htm