Pass the Tableau Desktop Specialist Desktop-Specialist Questions and answers with ValidTests

Explanation

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.

To work with the MOST up-do-date data, use a live connection instead!

As a general best practice, your pie chart should contain 2 to 5 categories. Anything more than that is not easy for the eyes to distinguish. This is a common question and mentioned in Tableau's own eLearning module as well!

See how to build a pie chart:

The following showcase how you can show mark labels. Using the Sample Superstore dataset:

1) Let's create a Bar chart showing the sales for each sub-category:

2) Now you can show labels by:

2.1) Click on Show Mark Labels Icon in the Toolbar ( easiest )

2.2) Drag Sales to the Text icon in the Marks Card:

2.3) Click on Analysis -> Show mark labels from the Tableau menu bar:

Explanation

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.

Explanation

The following are 2 correct ways to copy the worksheet visualisation as an image:

AND

Explanation

In a box and whisker plot:

1) The ends of the box are the upper and lower quartiles, so the box spans the interquartile range

2) The median is marked by a vertical line inside the box

3) The whiskers are the two lines outside the box that extend to the highest and lowest observations.

Explanation

Yes, it is possible to create worksheet-specific filters in Tableau.

When you add a filter to a worksheet, by default it applies to the current worksheet. Sometimes, however, you might want to apply the filter to other worksheets in the workbook.

Then, you can select specific worksheets to apply the filter to or apply it globally to all worksheets that use the same data source or related data sources.

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

The default behavior of Tableau when you add a measure to the view is to automatically apply an aggregation to that measure. The type of aggregation applied can vary depending on the context of the view. For example, if no other measures or dimensions affect the measure, Tableau might default to SUM. However, if there are other dimensions in the view that define the level of detail, the aggregation might be different to reflect the context.

Two correct methods for creating a visual group are selecting marks in the view and right-clicking a dimension in the Data pane. A visual group is a way of combining related members in a dimension field to create categories or segments in your data. For example, you can create a visual group by selecting several states in a map view and grouping them into regions. You can create a visual group by selecting one or more marks in the view and then clicking the group icon on the tooltip or on the toolbar. This will create a new group field in the Data pane with default names for each group based on their members. You can also create a visual group by right-clicking a dimension in the Data pane and selecting Create > Group. This will open the Create Group dialog box where you can select several members and drag them into groups with custom names8 The other options are not correct methods for creating a visual group. Clicking the drop-down arrow from the top of the Data pane will open a menu with options for creating new fields, folders, sets, bins, etc., but not groups. Dragging a dimension onto another dimension in the Data pane will create a hierarchy, which is a way of organizing data into different levels of detail, not groups9