Pass the Tableau Desktop Specialist TDS-C01 Questions and answers with ExamsMirror

You should use a union when you have two tables with similarly named columns of data that you want to combine. A union is a method for combining data by appending rows of one table onto another table. The tables that you union must have the same number of fields, the same field names, and the same data types2

Extracts would be more beneficial for analyzing historical prices where we won't be making use of any real time data being streamed. Same is the case for enormous datasets having billions of rows (extracts will be more efficient in analyzing subsets of such large data).

As for live stock prices, flight updates, real time updates from production or mission critical systems - having a live connection is the most logical choice, since we need access to the most fresh and recent data possible at all times!

Explanation

Tableau Public is a free service that lets anyone publish interactive data visualizations to the web. Visualizations that have been published to Tableau Public (“vizzes”) can be embedded into web pages and blogs, they can be shared via social media or email, and they can be made available for download to other users.

Check it out : https://public.tableau.com/en-us/s/

Explanation

Joins combine tables by adding more columns of data across similar row structures. 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

Blends, unlike relationships or joins, never truly combine the data. Instead, blends query each data source independently, the results are aggregated to the appropriate level, then the results are presented visually together in the view.

*Very important question*

If we select the labels in the view and then group, a new consolidated mark is created - in our case bar since we are talking about a bar chart in the question. See below:

Then on grouping, a new bar is created, and the colour of all bars remain the same.

Had we grouped by choosing the marks instead of the labels, the following would be the result:

you can change the data type of a field from the Data Source tab by clicking on the drop-down menu of the field or by clicking the data type icon of the field. You cannot change the data type of a field from the Data pane or by selecting and holding the field.

Explanation

To reach the correct answer, follow the steps below:

1) Drag Category, and sub-category to the row shelf. Drag Profit to the Column shelf

2) Click the Sort-ascending icon as shown above, to sort the profits from least to greatest as shown:

Click the 'Show mark labels icon'

As we can see, Fasteners has the least Profit in the Office Supplies Category, and hence is our correct answer!

Explanation

Not too tough. Follow along the steps:

Drag Color to Filter and choose Red:

3) Now to display the percentage correctly, lets format it. Click on the In Stock % pill in the Row shelf, and select format:

And your final view will look like :

Explanation

As the names suggest, if we subtract the order date from the shipping date, we simply get the number of days between these 2 dates.

We can these use this calculated field in our charts, and can use COUNT, SUM, AVG etc with them according to our need.

According to the official documentation:

Joins are a more static way to combine data. Joins must be defined between physical tables up front, before analysis, and can’t be changed without impacting all sheets using that data source. Joined tables are always merged into a single table. As a result, sometimes joined data is missing unmatched values, or duplicates aggregated values.

Joins -

1) Are displayed with Venn diagram icons between physical tables

2) Require you to select join types and join clauses

3) Joined physical tables are merged into a single logical table with a fixed combination of data

4) May drop unmatched measure values

5) May duplicate aggregate values when fields are at different levels of detail

6) Support scenarios that require a single table of data, such as extract filters and aggregation