Top Most Frequently Asked Tableau Interview Questions (with Short Answers) set 1

1. What are Dimensions and Measures?

Dimensions are categorical fields (Region, Customer).
Measures are numeric fields (Sales, Profit).

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2. What is the difference between Discrete and Continuous fields?

Discrete creates headers (Blue).
Continuous creates axes (Green).

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3. What is a Tableau Extract?

A compressed snapshot of data stored in Tableau for faster performance.

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4. What is the difference between Live Connection and Extract?

• Live: Queries database in real time.
• Extract: Uses stored snapshot data.

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5. What is a Hierarchy?

A drill-down structure such as:

Region → State → City

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6. What is a Set?

A custom subset of data members.

Example:

Top 10 Customers
Top 5 Products

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7. What is a Group?

Combines multiple dimension members into a single category.

Example:

Karnataka + Tamil Nadu = South India

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8. What are Calculated Fields?

Custom fields created using formulas.

Example:

SUM([Sales]) - SUM([Cost])

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9. What is an LOD Expression?

Calculates values independent of the visualization level.

Example:

{ FIXED [Customer ID] : SUM([Sales]) }

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10. What are the types of LODs?

• FIXED
• INCLUDE
• EXCLUDE

Example:

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

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11. What is a Context Filter?

A primary filter applied before other filters.

Used to improve dashboard performance.

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12. Difference between Table Calculation and LOD?

LOD	Table Calculation
Calculated before visualization	Calculated after visualization
Uses source data	Uses displayed data

Example:

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

vs

RUNNING_SUM(SUM([Sales]))

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13. What is Data Blending?

Combining data from multiple sources based on common fields.

Example:

Sales Data + Target Data

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14. Difference between Join and Blend?

Join

• Happens before aggregation.
• Same data source layer.

Blend

• Happens after aggregation.
• Different data sources.

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15. What are Relationships in Tableau?

Logical connections between tables introduced in Tableau 2020.2.

They preserve each table's granularity.

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16. What is Row-Level Security (RLS)?

Restricts data visibility by user.

Example:

USERNAME() = [User]

Users see only authorized data.

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17. What is Tableau Order of Operations?

1. Extract Filter
2. Data Source Filter
3. Context Filter
4. Dimension Filter
5. Measure Filter
6. Table Calculation Filter

Frequently asked in interviews.

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18. How do you improve Tableau Dashboard Performance?

• Use Extracts
• Reduce Quick Filters
• Use Context Filters
• Avoid heavy LODs
• Optimize joins
• Reduce marks

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19. What are Parameters?

User-defined dynamic inputs.

Example:

Top N Customers
Sales Threshold

Parameter Example:

IF SUM([Sales]) > [Threshold]
THEN "High"
END

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20. Difference between INCLUDE, EXCLUDE and FIXED?

FIXED

Ignores view level.

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

INCLUDE

Adds dimensions to the calculation.

{ INCLUDE [Customer] : SUM([Sales]) }

EXCLUDE

Removes dimensions from the calculation.

{ EXCLUDE [Customer] : SUM([Sales]) }

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Most Important Interview Topics (Asked Very Frequently)

1. LOD Expressions (FIXED, INCLUDE, EXCLUDE)
2. Joins vs Relationships vs Blending
3. Context Filters
4. Order of Operations
5. Row-Level Security (RLS)
6. Dashboard Performance Tuning
7. Table Calculations
8. Parameters
9. Sets
10. Extract vs Live Connection

These 10 topics alone cover a large share of Tableau interview questions for 3–10+ years of experience.

Tableau Scenario-Based Interview Questions 1-5

 

1. How would you calculate Year-over-Year (YoY) Growth for each Product?

Compare current year's sales with the previous year's sales at Product level.

(
SUM([Sales])
-
LOOKUP(SUM([Sales]),-1)
)
/
ABS(LOOKUP(SUM([Sales]),-1))

Compute Using: Year
Partition By: Product

This helps identify fast-growing and declining products.

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2. How would you identify customers who have increased their spending every year?

Check whether sales are continuously increasing year-over-year.

SUM([Sales]) >
LOOKUP(SUM([Sales]),-1)

AND

LOOKUP(SUM([Sales]),-1) >
LOOKUP(SUM([Sales]),-2)

This identifies loyal customers with growing business value.

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3. How would you calculate Basket Size (Average Items per Order)?

A common Retail and E-Commerce KPI.

SUM([Quantity])
/
COUNTD([Order ID])

Example:

• Quantity Sold = 10,000
• Orders = 2,000

Basket Size = 5 Items per Order

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4. How would you identify products frequently purchased together?

Use a self-join on Order ID.

Data Source Logic

SELECT
A.OrderID,
A.ProductName Product1,
B.ProductName Product2
FROM Orders A
JOIN Orders B
ON A.OrderID = B.OrderID
AND A.ProductName <> B.ProductName

In Tableau:

COUNTD([Order ID])

Higher counts indicate strong product affinity.

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5. How would you calculate Customer Retention Rate?

Customers who purchased this year and also purchased last year.

COUNTD(
IF YEAR([Order Date]) = YEAR(TODAY())
AND
{ FIXED [Customer ID] :
MIN(YEAR([Order Date]))
}
< YEAR(TODAY())
THEN [Customer ID]
END
)
/
COUNTD([Customer ID])

This is one of the most important KPIs in customer analytics and subscription businesses.

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Interview Follow-up

A senior Tableau interviewer may ask:

"When would you use Table Calculation vs LOD vs Custom SQL?"

Answer:

• Table Calculation → Running Total, Rank, Moving Average.
• LOD → Customer-level, Product-level fixed calculations.
• Custom SQL → Heavy transformations better handled before Tableau.
• Best Practice: Push large calculations to the database whenever possible for better performance.