10 Most Frequently Asked Tableau Interview Questions for Freshers in 2025

Tableau has become a go-to tool for data visualization and business intelligence. Whether you're a fresher, a career switcher, or an experienced analyst, preparing for Tableau interview questions can boost your confidence and help you ace your next opportunity.

In this blog, we’ve curated a list of frequently asked Tableau interview questions — from beginner to advanced level — along with clear explanations and examples.

1. What are different types of Filters and what is the order of their operations?

There are 6 types of filters in Tableau and their order of operation are as below:

• Extract Filter

• Data Source Filters

• Context Filter

• Dimension Filter

• Measure Filter

• Table Calculation Filter

2. What are Dimensions and Measures in Tableau?

In Tableau, data is divided into two main types:

1. Dimensions → Qualitative data (categories, text, or IDs)

2. Measures → Quantitative data (numbers used for calculations)

Understanding with an Example

Imagine you have a Sales Dataset with the following columns:

• Dimensions (Categories / Labels):

  • Order ID → A unique identifier (not a number used for math).

  • Customer Name → Name of the customer.

  • Region → Which part of the country they belong to.

• Measures (Numbers used for calculations):

  • Sales → A numerical value representing revenue.

  • Quantity → A count of items sold.

Key Differences in Simple Terms

In Tableau, Dimensions are usually blue and Measures are generally green.

3. What is discrete and continuous variable in Tableau?

Discrete Variables (Categorical Data)

Definition:

• Discrete variables take distinct, separate values.

• They create headers, labels, or categories in visualizations.

• Discrete fields appear blue in Tableau.

Example: Imagine a dataset with Sales Data:

Discrete Fields:

• Order ID → Unique values for each order.

• Customer Name → Different customer names.

• Region → Different geographic categories (East, West, North).

Continuous Variables (Numerical Data)

Definition:

• Continuous variables can take any numeric value within a range.

• They are used for aggregations, trends, and measurements.

• Continuous fields appear green in Tableau.

  
  

Example: Using the same dataset, continuous fields include:

• Sales → Any numeric value (500, 800, 600, etc.).

Key Differences: Discrete vs. Continuous in Tableau

4. What is LOD expression? Give example for each of them?

LOD expressions allow you to control the level of granularity at which calculations are performed, regardless of what’s displayed in the visualization.

There are three types of LOD expressions:

• FIXED → Defines a specific aggregation level.

• INCLUDE → Includes extra dimensions in aggregation.

• EXCLUDE → Removes dimensions from aggregation.

FIXED LOD

Definition:

• Aggregates data at a fixed level, independent of filters in the view.

• It ignores dimensions in the visualization unless explicitly mentioned in filters.

Syntax:

{ FIXED [Dimension]: Aggregation[Measure] }

Example:

Find total sales per region, even if the user filters by product

{ FIXED [Region]: SUM([Sales]) }

If you filter by Product, the total Sales per Region remains unchanged.

Use Case: When you need a constant value unaffected by filters (e.g., Total Customer Count per Region).

INCLUDE LOD

Definition:

• Aggregates data at the granularity of dimensions in the view + extra ones you specify.

• It is more flexible and works with visual-level filters. (Dimension and Measure Filters)

Syntax:

{ INCLUDE [Dimension]: Aggregation[Measure]}

Example:

Find average sales per customer, even if the visualization is at a higher level (e.g., Region-level view)

{ INCLUDE [Customer Name]: AVG([Sales]) }

Even if your view is at the Region level, Tableau will still calculate average sales per customer within each region.

Use Case: When you want to dynamically include extra detail but keep the existing granularity in visualization.

EXCLUDE LOD

Definition:

• Aggregates data by removing specific dimensions from the visualization.

• Useful when you need an aggregation at a higher level than what’s displayed.

Syntax:

{ EXCLUDE [Dimension]: Aggregation[Measure] }

Example:

Find total sales per region, removing individual cities from the calculation

{ EXCLUDE [City]: SUM([Sales]) 

If you are viewing Sales by City, this will instead show Sales by Region (since City is excluded).

Use Case: When you want to calculate values at a higher level than the current view (e.g., % of Total Sales per Region while showing City-wise sales).

Quick Comparison of LOD Expressions

When to Use Each?

✔ FIXED → When you need a constant, pre-defined aggregation.

✔ INCLUDE → When you want extra granularity but respect visualization structure.

✔ EXCLUDE → When you want to remove a dimension to get a higher-level aggregation.

5. Difference between Join and Relationship in Tableau? Which one is better?

Example to Understand

Join Example (Immediate Merge)

You have:

• Customers Table: Customer ID, Name

• Orders Table: Customer ID, Order ID, Sales

If you do a Join on Customer ID, Tableau will immediately merge these tables:

Customer ID | Name | Order ID | Sales

--------------------------------------

101 | John | O-001 | 500

101 | John | O-002 | 700

102 | Alice | O-003 | 800

Risk: If one customer has multiple orders, customer data is repeated (duplication).

Relationship Example (Smart Connection)

You connect the same tables with a relationship:

• Tableau keeps them separate.

• When you create a visual, like "Total Sales by Customer", Tableau builds the correct query.

• No duplication of data.

Cleaner, smarter, and Tableau manages the complexity for you!

6. What is Data Blending in Tableau?

1. Data blending is a method in Tableau to combine data from two different data sources that are not connected directly.

2. Unlike Joins and Relationships (which happen inside a single data source),

3. Blending is for situations when your data is coming from two separate sources — like Excel + SQL, or Google Sheets + Cloud database.

Simple Example:

• Data Source 1 (Primary): 

Table A
Country | Sales 
USA | 5000 
India | 7000 
UK | 4000 

• Data Source 2 (Secondary): 

Table B
Country | Sales
USA | 6000
India | 8000
Germany | 5000

You want to compare Sales vs. Target.

But:

• Sales data is in Excel.

• Target data is in a Google Sheet.

  
  

They’re in different sources, so you can’t do a normal Join.

Solution: Use Data Blending!

How Does Blending Work?

1. You bring both data sources into Tableau.

2. Define a common field — here it is Country.

3. Tableau uses the common field to blend the data visually in the worksheet.

In Tableau, you'll see:

• Primary Data Source (with a blue tick).

• Secondary Data Source (with an orange link icon 🔗).

Data Blending is like making a VLOOKUP in Excel between two files.

You're not physically joining the data — Tableau combines the summary results in the view.

When Should You Use Data Blending?

Important Points to Know:

• Blending happens after aggregation — Tableau first aggregates both sources, then blends.

• Primary data source drives the view.

• Blending does not create a single combined table — it blends at visualization level.

• Filters from primary source affect the view, but secondary source filters may need care.

7. What is a context filter?

A context filter is like a first-level filter in Tableau. When you have multiple filters, and you want Tableau to apply one filter first, and then apply others based on that filtered data, you use a context filter.

In short:

Why do we need it?

Normally, Tableau applies all filters independently. But sometimes, you want Tableau to filter the data first, then apply other filters on top of that filtered data.

Using context filters:

• Improves performance (especially with large data).

• Helps you create dependent filters.

Example:

Imagine you have a Sales dataset:

Now you apply 2 filters:

1. Region = Asia

2. Category = Furniture

Without Context Filter:

• Tableau checks all data for both filters independently.

With Context Filter:

• You set Region = Asia as a context filter.

• Tableau first filters data to only Asia.

• Then it applies the Category filter on the Asia data only.

Result: Faster and more accurate filtering!

8. What is the difference between Sets and Groups in Tableau?

 Example:

Scenario: You have Categories like:

• Chairs

• Tables

• Sofas

• Beds

• Desks

If you use a Group:

You can combine categories to make it simpler:

• Group 1: Furniture (Chairs, Sofas, Beds)

• Group 2: Office Furniture (Desks, Tables)

Now, Tableau treats them as new "grouped" categories.

If you use a Set:

You can create a set of Top 2 categories by sales.

• Set contains: "Tables", "Sofas"

Tableau marks records inside the set as True, others as False.

When to use?

9. What is Hierarchy in Tableau?

A Hierarchy in Tableau is a way to organize your data fields from higher level to lower level — so you can drill down or drill up in your visualizations.

It allows you to click and explore the data step by step!

Let’s say you have Sales data with:

• Country

• State

• City

If you create a Hierarchy like this:

Country > State > City

In your chart, you can first see Sales by Country.

Then, click on a country to drill down to see Sales by State.

And then, drill down further to see Sales by City.

It’s like zooming in to see details!

Why use Hierarchy in Tableau?

Summary:

10. What is Replace References?

In Tableau, "Replace References" is a feature that helps you swap out one field for another across your entire workbook without breaking anything.

Example:

Let’s say you built a dashboard using the field:

• Sales 2023

Later, your data updates, and now you have:

• Sales 2024

  
  

If you delete Sales 2023, Tableau will show errors everywhere it was used (charts, filters, calculations).

Instead of fixing each error manually, you can:

1. Right-click on the old field (Sales 2023).

2. Select "Replace References".

3. Choose Sales 2024.

   
   

Tableau will automatically update all uses of the old field to the new field across:

• Worksheets

• Filters

• Calculations

• Dashboards

  
  

✅ No errors! ✅ Saves time! ✅ No need to rebuild your charts.

When do you use it?