TDS-C01 Tableau Desktop Specialist Free Practice Exam Questions (2025 Updated)

You should use a relationship instead of a join for two data sets when the data sets include similar data aggregated at different levels of detail. A relationship is a way of combining data from different tables based on common fields without creating a single table with all fields. A relationship allows you to query data from multiple tables as needed and preserve the level of detail of each table. A relationship is useful when you have data sets that are aggregated at different levels of detail, such as sales by region and sales by product. A relationship can match data from different levels of detail without creating null values or duplicate rows. The other options are not valid situations for using a relationship instead of a join for two data sets. The data sets include similar data aggregated up to the highest level is not correct, because a relationship is not needed when both data sets have the same level of detail. You can use either a relationship or a join in this case, depending on your preference and performance. The data sets are in two separate tables within a single spreadsheet is not correct, because a relationship is not limited by the location or format of the data sets. You can use a relationship to combine data from different sources or connections, such as databases, files, or web services. To use both data sets across multiple sheets in a visualization is not correct, because a relationship does not affect how you use data in your visualization. You can use either a relationship or a join to create multiple sheets and dashboards with your data sets.

In Tableau, a Density map (also known as a heat map) uses automatically generated sizes for the data points that cannot be resized by using the Marks card. This chart type is designed to display density of data points on a map, where the size and color of the data points represent the concentration of data in that area.

From the official documentation:

The default view that you first see in the Data Source page canvas is the logical layer of the data source. You combine data in the logical layer using relationships (or noodles).

According to the [Tableau Desktop Specialist Exam Guide], a join is a way to combine table columns in relational databases by using shared values. A join can be inner, left, right, or full, depending on how the matching rows are included or excluded.

To perform a union in Tableau, the tables must have a related field with matching data types, and they must have the same number of fields. This allows the tables to be appended vertically in the data source. Different names or tables from different connections do not affect the ability to union the tables.

This is one of the most common questions on the Tableau Desktop Specialist Exam.

Wrong options -

1) Don't use backgrounds - This is not a solution. What if we want to use backgrounds? We can't just stop using backgrounds to solve this problem.

2) Decrease the font size - Do you think using a smaller font will make the text more readable? No right? Hence, this is wrong too.

All other options are ways recommended to make your text more readable!

Explanation

Whenever you add a measure to your view, an aggregation is applied to that measure by default. This default is controlled by the Aggregate Measures setting in the Analysis menu.

If you decide you want to see all of the marks in the view at the most detailed level of granularity, you can disaggregate the view. Disaggregating your data means that Tableau will display a separate mark for every data value in every row of your data source.

To disaggregate all measures in the view:

Clear the Analysis >Aggregate Measures option. If it is already selected, click Aggregate Measures once to deselect it.

Explanation

It is possible to open a Tableau support case by visiting the following link : https://www.tableau.com/support/case

This is a tricky question often asked in the exam.

If we drag a new measure to the row shelf, the following happens:

We now have 2 rows, and the same 7 columns for both these rows. Therefore, 2x7 = 14 marks!

But if we drag the same measure to the column shelf, we have just 1 row and a chart created for each of the columns. So (1x7) = 7 marks!

Reference and notes: https://medium.com/@justindixon91/tableau-specialist-exam-notes-part-4-understanding-tableau-concepts-f78de83fdd35

According to the official Tableau documentation:

Explanation

THIS IS A VERY IMPORTANT QUESTION

To answer this question, you need to understand Tableau's Order of Operations. See below and remember this always:

Discrete measures and discrete dimensions appear blue in Tableau. Discrete fields are those that have a finite number of distinct values, such as names, categories, or dates. Discrete fields are usually used to create headers or labels in the view. Blue fields indicate that the field is discrete. Continuous measures and continuous dimensions appear green in Tableau. Continuous fields are those that have an infinite range of possible values, such as numbers or ratios. Continuous fields are usually used to create axes or color gradients in the view. Green fields indicate that the field is continuous1

Names and Categories would be mostly used as dimensions (categorical data).

Profit and measures contain quantitative data and would be more suitable for Measures!

Explanation

The data that you analyze in Tableau is often made up of a collection of tables that are related by specific fields (that is, columns). Joining is a method for combining data on based on those common fields. The result of combining data using a join is a virtual table that is typically extended horizontally by adding columns of data.

For example, consider the following two tables originating from a single data source:

We can combine these 2 tables, simply by joining the tables on ID to answer questions like, "How much was paid in royalties for authors from a given publisher?". By combining tables using a join, you can view and use related data from different tables in your analysis.

In Tableau, to create a reference line, one of the axes in the view must contain an aggregated measure. Reference lines are typically used to add context or additional information to the data in a view, and they are often based on some form of aggregate such as the average, median, sum, etc., of a measure.

You can adjust the size for all charts except the Tree Map. You use dimensions to define the structure of the treemap, and measures to define the size or color of the individual rectangles. Treemaps are a relatively simple data visualization that can provide insight in a visually attractive format.

In a Tree Map, the measure itself defines the size and colour! The greater the sum of Measure for each category, the darker and larger its box.

Dashboard actions in Tableau are interactive elements that can be used to create a more dynamic experience. These actions can be set to filter data on the dashboard, highlight specific elements, or even direct the user to a URL when interacting with a visualization. They can be triggered by user interaction such as hover, selection, or accessing a context menu. Moreover, dashboard actions can have multiple source sheets (where the action originates) and target sheets (where the action takes effect), allowing for a complex interactivity between different parts of the dashboard.

Explanation

Follow along to get the correct answer:

Drag Country to the view, and then Patents to the Size Mark on the Marks shelf as follows :

2) But, this isn't all right? We need to focus on the year 2013. This can be done by using the year column as it is (continuous) in the filter shelf, or by converting it to discrete first and then using it:

2.1 As it is (continuous)

2.2 Converting to discrete first & then using it

and then :

3) We can now see that France, with 485 has the 2nd most number of patents for the year 2013

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.