If you’re preparing for analytics or BI roles, you already know that interviews often go deep into DAX and data modeling. Recruiters don’t just test whether you can build dashboards — they want to see how well you understand relationships, calculations, and performance optimization.

This blog covers the most common power bi dax interview questions and key areas asked in a power bi data modeling interview. The explanations are written in a simple, interview-friendly way so you can confidently explain concepts instead of memorizing definitions.

Whether you’re revising dax formulas interview prep topics or getting ready for power bi advanced questions, this guide will help you understand the logic behind answers — which is exactly what interviewers look for.

Power BI DAX Interview Questions and Answers

1. What is DAX in Power BI?

Answer: DAX (Data Analysis Expressions) is a formula language used in Power BI to create calculated columns, measures, and calculated tables. It helps you perform calculations on your data model.

In interviews, don’t just define it. Explain that DAX works on columnar data and is optimized for analytical queries. It is mainly used for aggregation, filtering, time intelligence, and advanced calculations inside the Power BI data model.

2. What is the difference between a calculated column and a measure?

Answer: This is one of the most common power bi dax interview questions.

A calculated column:

  • Is computed row by row.
  • Stored in memory.
  • Increases model size.
  • Used when you need row-level logic.

A measure:

  • Calculated on the fly.
  • Not stored physically.
  • Depends on filter context.
  • Used for aggregations in visuals.

Interview tip: If logic depends on user interaction or filtering, prefer measures.

3. What is context in DAX?

Answer: Context is a core concept in dax formulas interview prep.

There are two main types:

Row context
 Row context exists when DAX evaluates one row at a time. It’s common in calculated columns.

Filter context
 Filter context comes from slicers, filters, and visuals. Measures work mostly with a filter context.

If you clearly explain the difference between row context and filter context, you’ll stand out in Power BI advanced questions.

4. What is the CALCULATE function and why is it important?

Answer:  CALCULATE is one of the most powerful DAX functions. It modifies the filter context and evaluates an expression under new filters.

Example:
 CALCULATE(SUM(Sales[Amount]), Sales[Region] = “East”)

In interviews, mention that CALCULATE:

  • Changes filter context
  • Enables advanced business logic
  • Is essential for dynamic measures

It is frequently discussed in bi modelling interview topics.

5. What are common DAX aggregation functions?

Answer: Common aggregation functions include:

  • SUM
  • AVERAGE
  • COUNT
  • DISTINCTCOUNT
  • MAX and MIN

Interviewers may ask about DISTINCTCOUNT because it behaves differently from COUNT.

Make sure you explain when to use each function and how they interact with the filter context.

6. What are time intelligence functions in DAX?

Answer: Time intelligence functions help analyse data over time.

Common examples:

  • TOTALYTD
  • SAMEPERIODLASTYEAR
  • DATEADD
  • DATESYTD

Interview tip: Mention that a proper date table is required for time intelligence functions to work correctly. This is often part of power bi data modelling interview discussion.

7. What is the difference between ALL and ALLEXCEPT?

Answer: ALL removes all filters from a table or column.

ALLEXCEPT removes filters except the ones specified.

Example use case:
 If you want to calculate the percentage contribution ignoring product filters but keeping region filters, you use ALLEXCEPT.

8. What is data modelling in Power BI?

Answer:  Data modelling is the process of defining relationships between tables so that data can be analysed efficiently.

A good data model:

  • Reduces redundancy
  • Improves performance
  • Makes DAX simpler
  • Supports scalability

In interviews, always mention performance and maintainability.

9. What is a star schema?

Answer: A star schema consists of:

  • One fact table
  • Multiple dimension tables

The fact table contains transactional data (like sales).
 Dimension tables contain descriptive data (like product, date, customer).

Star schema is preferred in power bi data modeling interview discussions because:

  • It improves performance
  • It simplifies DAX
  • It ensures clarity

10. What is the difference between star schema and snowflake schema?

Answer: Star schema:

  • Dimensions are not normalized
  • Simpler structure
  • Better performance

Snowflake schema:

  • Dimensions are normalized into multiple related tables
  • More complex
  • Slightly slower performance

Interview tip: Mention that Power BI generally works better with star schema.

Conclusion

Preparing for Power BI DAX interview questions requires more than memorising formulas. Interviewers test how well you understand context, relationships, and modelling logic. Strong DAX formulas interview prep, combined with solid data modelling knowledge,e will help you answer Power BI advanced questions confidently.

If you focus on core BI modelling interview topics like star schema, relationships, filter context, and CALCULATE behaviour, you’ll be well-prepared for technical discussions.

Remember: A clean data model makes DAX simple. A poor data model makes DAX complicated.