Desktop-Specialist Exam Dumps - Tableau Desktop Specialist Exam

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.

 A dual axis chart is useful for comparing two measures that have different scales. A dual axis chart is a type of visualization that shows two measures using two independent axes layered on top of one another. A dual axis chart allows you to compare and contrast two measures that have different ranges or units of measurement, such as sales and profit margin, temperature and precipitation, or population and GDP per capita. A dual axis chart can also show different mark types for each measure, such as bars and lines, circles and areas, or shapes and texts. The other options are not valid reasons for using a dual axis chart for comparing two measures. Have little in common is not correct, because a dual axis chart is meant to show some kind of relationship or correlation between two measures, not just contrast them. Are Table Calculations is not correct, because a dual axis chart can be used with any type of measure, whether it is an aggregation, a calculation, or an expression. Are aggregated Dimensions is not correct, because a dual axis chart cannot be used with dimensions, only with measures. Dimensions are fields that contain qualitative values that are used to categorize or segment data, not compare them.

Three statements that accurately describe dimensions are: Contain qualitative values Affect the level of detail in the view Can be continuous or discrete Dimensions are fields that contain qualitative values, such as names, dates, or geographical data. You can use dimensions to categorize, segment, and reveal the details in your data. Dimensions affect the level of detail in the view by creating headers or labels for marks or by defining how data is partitioned for table calculations. Dimensions can be continuous or discrete depending on their data type and format. Continuous dimensions are those that have an infinite range of possible values, such as numbers or ratios. Discrete dimensions are those that have a finite number of distinct values, such as names, categories, or dates3 The other options are not accurate statements about dimensions. Contain numeric, quantitative values is not correct, because fields that contain numeric, quantitative values are measures, not dimensions. Measures are fields that contain numeric values that you can measure and aggregate. Affect the formatting options in the view is not correct, because formatting options are not determined by dimensions, but rather by user preferences and choices. Formatting options allow you to change the appearance of various elements in the workbook, such as fonts, borders, shading, alignment, etc3

Explanation

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.

Explanation

The word 'static tool' gives it away.

According to the official website :

Explanation

A geographic role associates each value in a field with a latitude and longitude value.

Assigning a geographic role based on the type of location (such as state versus postcode) helps ensure that your data is plotted correctly on your map view. For example, you can assign the City geographic role to a field that contains a list of city names.

To assign a geographic role to a field:

In the Data pane, click the data type icon next to the field, select Geographic Role, and then select the geographic role you want to assign to the field.

When you assign a geographic role to a field, Tableau adds two fields to the Measures area of the Data pane: Latitude (generated) and Longitude (generated).

These fields contain latitude and longitude values and are assigned the Latitude and Longitude geographic roles. If you double-click each of these fields, Tableau adds them to the Columns and Rows shelves and creates a map view using the Tableau background map.

To show how Internet Usage values change from year to year as a percentage, you should apply the "Percent Difference" quick table calculation to the Internet Usage field. This calculation compares each value to the previous value and computes the difference as a percentage, which is ideal for analyzing the rate of change over a sequential time period such as consecutive years.

Explanation

By default, all filters that you set in Tableau are computed independently. That is, each filter accesses all rows in your data source without regard to other filters. However, you can set one or more categorical filters as context filters for the view. You can think of a context filter as being an independent filter. Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter.

You may create a context filter to:

1) Improve performance – If you set a lot of filters or have a large data source, the queries can be slow. You can set one or more context filters to improve performance.

2) Create a dependent numerical or top N filter – You can set a context filter to include only the data of interest, and then set a numerical or a top N filter.