Free TDS-C01 Practice Test Questions and Answers (2026) | Cert Empire Practice Questions

Bin are a user-defined grouping of numerical data in the data source.

According to the official Tableau documentation: It's sometimes useful to convert a continuous

measure (or a numeric dimension) into bins.

Have a look at the following image. When we right click a measure, we get the following options:

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However, for a dimension (this is because the DATA TYPE of this dimension is a string:

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But what if we have a dimension of type NUMBER (NUMERIC DIMENSION)? See below:

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We can clearly create bins from dimensions too - they just have to be numeric :)

For more information, please refer to : https://help.tableau.com/current/pro/desktop/enus/calculations_bins.htm

Yes, it is possible to disable highlighting for the entire workbook.

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For more information : https://help.tableau.com/current/pro/desktop/en-gb/actions_highlight.htm

You can show trend lines in a visualization to highlight trends in your data.

To add trend lines to a view, both axes must contain a field that can be interpreted as a number. For

example, you cannot add a trend line to a view that has the Product Category dimension, which

contains strings, on the Columns shelf and the Profit measure on the Rows shelf.

However, you can add a trend line to a view of sales over time because both sales and time can be

interpreted as numeric values.

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

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.

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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.

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

Yes! We get different color palettes. They are:

*From the official Tableau documentation*

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To change the color for a value

1) Click on an item on the left, under Select Data Item.

2) Click a new color in the palette on the right. In Tableau Desktop you can hover over a swatch to

identify the color.

3) Repeat for as many values that you want to change.

4) In Tableau Desktop, click OK to exit the Edit Colors dialog box. In Tableau Server or Tableau Online,

simply close the dialog box.

AND

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Reference: https://help.tableau.com/current/pro/desktop/en

us/viewparts_marks_markproperties_color.htm

The INNER JOIN keyword selects all rows from both tables as long as there is a match between the

columns. Consider 2 tables "Orders" and "Customers".

If there are records in the "Orders" table that do not have matches in "Customers", these orders will

not be shown!

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

In fact, the opposite of this is true.

Trick : Whenever you think of joins -> Think that after the join is created, we get 1 single flat

combined (joined) table. This flat combined table is created prior to us creating our visualizations.

This happens at the physical layer.

If you ever think about relationships, know that all tables will remain distinct and separate, and

relationships sit at the logical layer. At run time, when you bring in the dimensions and measures to

create your viz, Tableau very smartly creates the necessary joins, relates the tables and sends queries

to these tables to get the resultant data back in the most meaningful way possible. This allows you to

focus on using your data and revealing insights from it and focus less on the data preparation aspect!

Refer to logical layer vs physical layer from the official

documentation: https://help.tableau.com/current/server/en-us/datasource_datamodel.htm

You can add a reference line to any continuous axis in the view.

To add a reference line:

Drag Reference Line from the Analytics pane into the view. Tableau shows the possible destinations.

The range of choices varies depending on the type of item and the current view.

In a simple case, the drop target area offers three options:

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

Yes! To concatenate fields, they must be of same data type. However, there is a workaround which

we can use - Type casting. See below:

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Here, State and City are Strings, but Postal Code? Nope. It's an Integer. So we can simply use the

STR() function to convert it into a String, and hence the entire equation becomes valid!

Yes! It is possible to duplicate a field and add it to more than one hierarchy. Right click and choose

duplicate.

Reference: https://www.tableau.com/about/blog/2016/8/take-note-these-10-handy-tableaushortcuts-57561

It is possible in Tableau to use a sheet within a story directly.

Moreover, in Tableau, a story is a sequence of visualizations that work together to convey

information. You can create stories to tell a data narrative, provide context, demonstrate how

decisions relate to outcomes, or to simply make a compelling case.

At the same time, a story is also a collection of sheets, arranged in a sequence. Each individual sheet

in a story is called a story point.

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

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

According to the official documentation, the following are the requirements for using relationships:

1) When relating tables, the fields that define the relationships must have the same data type.

Changing the data type in the Data Source page does not change this requirement. Tableau will still

use the data type in the underlying database for queries.

2) You can't define relationships based on geographic fields.

3) Circular relationships aren't supported in the data model.

4) You can't edit relationships in a published data source.

5) You can't define relationships between published data sources.

6) Your workbook must use an embedded data source for you to be able to edit relationships and

performance options in the Data Source page in Tableau Online or Tableau Server.

Reference: https://help.tableau.com/current/online/enus/datasource_relationships_learnmorepage.htm

Yes! We get different options for filtering depending on whether we use a categorical data (think

dimension) or quantitative data (think measure).

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Important Note: Each tab adds additional definitions to your filter. For example, you can select to

exclude values under the General tab, and also add limits under the Top tab. Selections and

configurations from both tabs are applied to your filter.

At any time, you can see the definitions of your filter under Summary on the General tab.

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Note: If you have a large data source, filtering measures can lead to a significant degradation in

performance. It is sometimes much more efficient to filter by creating a set containing the measure

and then apply a filter to the set. For more information about creating sets, see Create Sets

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

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:

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

Sets are custom fields that are created within Tableau Desktop based on dimensions from your data

source. They are subsets of your data, which can be created manually or computed. Either

dimensions or measures can be used to determine what is included or excluded from a set using

conditional logic, but to CREATE a set we use dimensions.

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Reference : https://interworks.com/blog/rcurtis/2016/10/26/tableau-deep-dive-sets-introductionsets/

We can very much blend multiple measures into a single axis. Such charts are called Combined-Axis /

Blended-Axis charts. Follow along:

Measures can share a single axis so that all the marks are shown in a single pane. To blend multiple

measures, drag one measure or axis and drop it onto an existing axis.

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Instead of adding rows and columns to the view, when you blend measures there is a single row or

column and all of the values for each measure is shown along one continuous axis. For example, the

view below shows quarterly sales and profit on a shared axis.

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Note: If you drag a measure on to the canvas and only see a single ruler indicator instead of the

double ruler indicator shown below, Tableau creates dual axes instead of a blended axis. For more

information about how to create dual axes, see Compare two measures using dual axes.

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

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.

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Reference: https://help.tableau.com/current/pro/desktop/en-us/calculations_aggregation.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 is possible to join a maximum of 32 tables in Tableau!

Reference: https://www.mytectra.com/interview-question/tableau-interview-question-and-answers