Tableau Interview Questions

Follow the top Tableau practical interview questions listed here and turn yourself into an essential Tableau Developer. This will fast-track your career and help you land the best jobs as a Tableau developer, Tableau Administrator, Data Analyst,etc . We will start with a few basic interview questions about Tableau like Tableau file types, different data types, etc. and move on to advanced topics like symbol map, trend lines, reference lines, etc. If you already have interviews lined up, go through the following Tableau interview questions and prepare in advance.

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Tableau product families include the following: Tableau Desktop, Tableau Prep, Tableau Online, Tableau Server, Tableau Mobile, and Tableau Public.

There are different offerings from product family point of view for individuals, for organizations etc.

For individuals: Typically Tableau Desktop, Tableau Prep, Tableau Server, Tableau Online are being leveraged based on pricing options.

For organizations: Tableau offers deployment options such as on-premise or public cloud and fully hosted by tableau. In each of these segments, options vary such as Tableau Creator (Tableau Desktop, Tableau Prep, Tableau Online), Tableau Explorer (Tableau Online) and Tableau Viewer (Tableau Online) etc.

Tableau Desktop – primarily used for visual analytics is a self-service analytics and visualization product. We can connect to multiple data sources directly and perform live data analysis. Data can also be integrated from multiple data sources and dashboard can be created from those.

Tableau Prep – it is used for data preparation, data analysis by helping people quickly, confidently combine data, transform them, and clean them.

Tableau Online – this is fully hosted in the cloud. We can publish dashboards and share our discoveries with anyone. All easily accessible from a web browser or on the go with mobile applications.

Tableau Server – this is primarily required at a large Enterprise level in an organization. We can create and publish dashboards using Desktop and share them across the organization using this Server. Web-based server is available for easy and quick access.

Tableau Mobile – this is a very unique way of getting quick access to data on mobile devices. Based on access privilege, relevant details can be seen. Selecting, filtering, drill down features of data are available on this. It can be downloaded from Google playstore or Apple Appstore.

Tableau Public – One can create and share interactive charts, graphs, maps, mobile-friendly dashboards using this and can publish anywhere on the web.

We can save and share data using a variety of different Tableau File Types. The difference between each file type relate to the amount and type of information stored in the file. Following are different file types:

  • Tableau Workbook (twb) – tableau’s default way to save data. Information to visualize data. No source data involved. Typically this is an xml document format.
    • Key objective is to capture entire workbook which may contain one or multiple worksheets
    • This may also additionally contain dashboards
    • This may also contain the stories
    • All sheets, connection information stored. But data not stored

  • Tableau data source (tds) – typically accesses frequently used data sources. It includes server address, password, other metadata related details to the data source.
    • Key objective is to store all information for connecting to a data source
    • However, it does not contain the actual data

  • Tableau bookmark (tbm) – this is typically sharing worksheets from one workbook to another. It includes information to visualize and the data source provided the source workbook is a packaged workbook.
    • As the name reflects, key is to use it as a bookmark
    • Contains information for one worksheet
    • Simple and easy way to share work quickly through the bookmark if objective is to share a particular worksheet only

  • Tableau data extract (tde) – it improves performance and enables more functions. It includes source data as filtered and aggregated during extract.
    • Key for storing data source related information and hence users can use it to work in offline mode

  • Tableau packaged workbook(twbx) – this is typically shared with all who do not have access to source data and can view entire dashboard with the help of a tableau reader.
    • Key objective is to store all information, data, local files, images all in a zip file format in this and share to other users who may not have access to Tableau so that they can view
    • All sheets, connection information stored. Data is also stored
    • This format takes more space since it captures data, but very useful as it stores all necessary information. Can be used best for backing up your work

  • Packaged data source (tdsx) – this is typically a zip file format which contains the data source file of .tds extension and local file data sources such as .tde files, text files, excel files, MS access files etc.

Out of the above, tde and twbx files generally handle potentially large data.

Tableau supports below data types:

  • Text values
  • Date values
  • Date and time values
  • Numerical values
  • Geographical values (latitude and longitude used for maps)
  • Boolean values (true/false, 0/1 type of conditions)

Data type can be changed for a data type either on the “Data source” page or in the “Data” pane in Tableau. For example, if current data type is defined as “text value” and this needs to be changed, then data type in “Data source” page will provide dropdown list options of other data types such as “Number (decimal)”, “Number (whole)”, “Data & Time”, “Date”, “String”, “Boolean”. By default, the data type would be defined as a “Text value”.

It is important to change the data type so that it reflects correctly while we generate an extract.

When Tableau reads from data sources such as files in Microsoft excel or csv (comma separated value) formats, then it assigns data type automatically for respective values such as text/strings, date field, date and time field, Boolean field and so on. Additionally, if we source data from a csv with some mixed-value column is mapped to a single data type field in Tableau, then Tableau may assign one of the data type. For example a mixed value could be a mixture of numbers and dates, and they can be mapped to a number data type or date data type.

Suggested approach is to handle mixed value columns in a way one can format empty cells within the data source so that data type of that field can be matched and also to create additional new field that does not contain mixed values so that it is of a particular data type.

Tableau support below aggregation types: sum, average, median, count, count distinct, standard deviation, variation, minimum, maximum, standard deviation of a population, variance of a population, attribute (ATTR), dimension.

Aggregation in Tableau can be performed on measures and dimensions both. It is more common in measures. When we create a measure in the view, aggregation is already applied to its values by Tableau. So “Revenue” will become “Sum(Revenue)”. Typically “Sum” is the default. Else other aggregations such as Average, Median, Count, Count(Distinct), Minimum, Maximum, Percentile, Std. Dev, Std. Dev(Pop.), Variance, Variance(Pop.) can also be selected from the list. These options are primarily for measures. For dimensions, we can have aggregation types as minimum, maximum, count, count distinct etc.

When we have multiple data sources and we want to integrate those to visualize in our Tableau dashboard, we use blending.

  • It creates a connection between primary and secondary data source by a left join kind of a link.
  • By default, Tableau does a left join considering all data elements from primary data source.
  • Shuffling of various options can be performed between data sources. There are two ways of blending – automatically defined relationship and manually defining the blend.

Each data source contains its set of dimensions and measures. We can not have specific set of dimensions and measures which will limit us to create groups, formulas etc across the data. Data sources can be of multiple RDBMS such as Oracle, SQL Server, DB2 etc. and other data sources such as spreadsheets etc.

Blending also avoids joining of duplicate data as it will be linked via a common dimension.

One can change the linking field (dimension or measure) or add more linking fields to include additional rows of data from the secondary data source in the blend, changing the aggregated values.

Yes Time series is possible in Tableau and can be visualized using Line and Area charts. Both the charts can be represented in continuous and discrete format.

We can use line charts to display time series data in Tableau. We can add the dimensions, create measures and display it based on a time value (e.g. let’s say by year, month, week, day etc.). We can turn on the forecasting feature to see if we can get a feel of the seasonal trend as part of forecasted model. In Analytics pane, there is an option called “Forecast” to be able to see the Forecast. There will be some default period for which it will show the forecasted values.

Using these charts different level of granularity (e.g. monthly, weekly, daily etc.) can be shown. Different forms of aggregation can be performed.

Bullet graphs are bar charts that include a reference line and reference distribution for each cell in the plot. These are used to pack a lot of information into a small space.

Bubble charts offer another way to present one-to-many comparisons by using size and colour. They can be interesting to look at but do not allow for very precise comparisons between the different bubbles.

Hence bullet graph and bubble chart are completely different and used for very different purposes.

Tableau workbooks can be shared securely via Tableau Server. When we want to share dashboard with data and someone who is supposed to view does not have access to Tableau Server and can only view via Tableau reader, then file has to be saved with data as .twbx file format.

Tableau Public can also be used where users can create dashboards and share / publish to other users. Of course these will include – user level filter criteria, database level security, application or environment level security etc. to be able to show relevant data.

There are options to restrict sensitive data at the time of extracting file using extract data dialog box.

There are multiple ways to sort data in Tableau.

  1. Manual sorting via icons which appear in toolbar menu.
  2. Calculated sorts using the sort menu.
  3. Sorting using legends in a visualization chart.
  4. Sorting on multiple dimension views.

There are a few different ways to add filters to data visualization.

  1. Normal filter - Dragging any dimension or measure on to the filter shelf provides filtering that is accessible to the designer. That is a normal filter.
  2. Quick filter - When normal filter is used as a generic filter by making it accessible to more people by turning it into a quick filter. This can be reused by all.
  3. Context filter - Context filters do not only filter the data, they cause Tableau to create a temporary table that contains only the filtered data. Typically, if we plan to alter our filter frequently, then it is advisable not to use context filters.
  4. Data source filter – this can be applied at data source level to visualize / restrict which dimension, measures etc. to show from a data source or multiple data sources.

Normal filter is used to restrict data from database based on specific dimension / measure etc. that is selected / specified by the user.
Quick filter can be used in an adhoc format. Here user has the option to dynamically change data members at run time. Restricted data members can not be seen here.


Auto scrolling filters are not supported in Tableau Server, but they can be consumed in Tableau Desktop or Tableau Reader.

Step by step process of adding auto filters can be described below:

  • Go to “worksheet”
  • Select “action” (this could be select, hover, menu etc.)
  • Click “add action”
  • Select “filter” (basically there are 3 options under add actions – filter, highlight and URL)

Selecting filter here adds filtering action to the set of data used.

  • Give the name of the “filter”
  • Select “source sheet”

Essentially auto filters are useful when some action is triggered in the source sheet, then that reflects in the target sheet where there is a correlation of data shown or linked.

When using “action”, by hovering filters automatically gets applied to target sheet if we have defined as “hover” option, and so on.

Any field placed on the filter shelf enables a filter for that dimension or field. The style of filter control is dependent on whether the field is continuous or discrete. If we want to expose a filter in the worksheet, we can right-click on any pill used anywhere in the workspace and then select the menu option Show Quick Filter.

Granular details can be displayed with the help of symbol maps. Proportional values can also be represented using these maps which will help show quantitative values for individual ids. So if we want to show sales data for last 5 years across the country, the same can be shown in the geo-view with proportionate size / magnitude related to each and every location accordingly.

Filled up at the other hand is more suitable for showing “ratio” data. Pie charts typically have that information to a relative whole. For example, we want to display percentage of sales for a product by geographic location. In that case, filled maps can be used to show percentage of sales for different categories individually and relative to total sales by country.

From Symbol Maps standpoint:

  • Both data and map should be appealing enough and clear for user to interpret answers out of it
  • Proportion of varying magnitude in data can be shown
  • For example: we can plot earthquakes around the world and size them by magnitude.

From Fill Maps standpoint:

  • Here also both data and map should be appealing and clear enough
  • Relevant for showing “ratio” data
  • For example: when we want to show “cancer rate” around states, counties across a country for a particular time frame or within a time window, then fill maps or filled maps can be useful.

Both of them are different and not similar.Reference lines in Tableau are used to show visual comparisons to benchmark certain figure, constant or values. These can be added at a constant or computed value on the axis. For example, we are showing sales of Firm1, Firm2 and Firm3; the reference line can be set as an “average sales figure” which could act as a benchmark.

Trend lines on the other hand can be used to represent pattern of the trends in the data. For the same above example of Sales of a Firm1, we can use different trend lines to represent if it is following a particular trend. These can be linear trend, logarithmic trend, exponential trend, polynomial trend, power trend etc.

False. It is critical and important to build cascaded dashboard design.

Typically if input source data is huge, it will always be challenging to display the same in a dashboard. Hence it is advisable to have cascaded dashboard design where multiple views can be defined and cascaded in a dashboard to be able to provide summary view on top, other aspects on bottom left, bottom right etc with actions / filters to display required data appropriately.

tabcmd” and “tabadmin” are command line tools that Tableau uses.

“tabcmd” provides functions for performing workflow tasks like publishing workbooks, adding users, exporting workbooks, exporting data files etc.

“tabadmin” is used for server administration where tasks could be configuration of server options / server parameters, activating users, resetting passwords, managing deployments etc.

Answer should be b and d. As a and c are CORRECT answers.
As a best practice, we should remove all non-data ink. Hence d is INCORRECT.
As a best practice, we should use actions to filter data instead of quick filters. Hence b is INCORRECT.


Tableau is a fast-growing data visualization too. It is very popularly used in the Business Intelligence industry, as it simplifies raw data into easy, understandable format. The visualisations formed by Tableau are in the form of worksheets and dashboards. Non-technical users can create a customised dashboard as well, no coding is required for the same. The best features include data blending, collaboration of data and real-time analysis.

Candidates can opt to become a Tableau Developer, Business Intelligence Developer, Tableau Analyst, etc.
According to, professionals who are working as a Tableau Developer make an average of $107,311 per year.
Top companies who use Tableau extensively are ZS Associates, Tableau software inc., Verizon, etc.

The following is a collection of the most commonly asked tableau interview questions by interviewers. It’s important to be prepared to respond effectively to the questions that employers typically ask in an interview. Since these tableau interview questions are commonly asked, your prospective recruiters will expect you to be able to answer all of them. These tableau interview questions will increase your confidence to ace the next interview.

Going through these interview questions for tableau will help you land your dream job and will definitely prepare you to answer the toughest of questions in the best way possible. These basic and advanced level tableau interview questions are suggested by experts and have proven to be of great value. A few tricky questions are also discussed in the following sections, starting from the intermediate level.

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