Power BI Interview Questions

General Power BI Concepts

1. Explain the difference between Power BI Desktop, Power BI Service, and Power BI Report Server.
2. What are the different types of filters in Power BI, and how do they differ?
3. How does Power BI handle data refresh, and what are the main refresh types available?
4. Can you explain the difference between Import Mode, DirectQuery, and Live Connection in Power BI?
5. What is row-level security (RLS), and how do you implement it in Power BI?
6. How do you handle large datasets in Power BI, and what techniques can optimize performance?

Data Modelling and DAX

7. Explain the importance of star schema in Power BI data modelling. When would you consider a snowflake schema?
8. What is a calculated column vs. a measure, and when would you use each?
9. Describe DAX functions you commonly use. Can you explain FILTER, CALCULATE, and RELATED functions?
10. How would you write a DAX formula to get a running total?
11. Explain context transition in DAX. How does it affect the way functions like CALCULATE behave?
12. Can you describe what ALL and ALLEXCEPT do in DAX? Provide an example of when you’d use them.
13. How do you handle date and time calculations in Power BI? What are common DAX functions used in time intelligence?

Advanced Power BI Visualizations and Reporting

14. What are the ways to create drill-through or drill-down functionality in Power BI?
15. Explain how you’d create dynamic titles for visuals based on slicer selections.
16. What options do you have to enable user interactions in a Power BI report (e.g., bookmarks, tooltips)?
17. How would you handle conditional formatting in Power BI? Can you give examples of using conditional formatting for values, colors, etc.?
18. Explain the role of bookmarks in Power BI. How would you use them to create interactive storytelling?
19. How can you display only selected filtered values in a bar graph without dimming the others (like you wanted in one of your projects)?

Power BI Performance Optimization

20. What are some best practices for optimizing Power BI report performance?
21. How would you optimize a Power BI model with millions of rows for quick filtering and aggregation?
22. What are composite models, and how do they help in performance tuning?
23. Explain query folding. How does it impact performance, and what are the best practices for maintaining query folding?
24. When would you use aggregations in Power BI, and how would you set them up?

Power BI and Integration with Other Tools

25. How do you set up Power BI integration with external databases like SQL Server or MySQL?
26. Describe your experience connecting Power BI to Snowflake. How do you manage data connectivity and refreshes for large datasets?
27. Can you explain how you would publish Power BI reports to SharePoint, Teams, or embed them in an application?
28. How do you handle version control with Power BI reports, especially if you work with a team?

Scenario-Based and Troubleshooting

29. Describe a complex report/dashboard you’ve built. What were the challenges, and how did you resolve them?
30. How would you troubleshoot performance issues in Power BI reports?
31. Imagine your report shows unexpected results. What steps would you take to investigate and resolve the issue?
32. How would you handle a requirement where different users need to see different data in the same report?

Security and Deployment

33. What are the different ways to implement security in Power BI?
34. How do you implement row-level security for multiple tables in Power BI?
35. How do you ensure data governance and security when sharing reports within and outside your organization?

Power BI in Real-World Projects

36. Describe a project where you used Power BI to provide critical insights. How did it impact the business?
37. How do you approach building a dashboard for stakeholders who may have little knowledge of the data?
38. Tell us about a time when you used Power BI in a microfinance or banking-related project (if relevant). How did you address specific industry challenges?
39. What strategies would you use to create a scalable Power BI environment for a large enterprise?

Analytical and Problem-Solving Scenarios

40. If given a dataset, how would you approach calculating key metrics like Monthly Recurring Revenue (MRR) or Customer Retention Rate?
41. What steps would you take to design a report that shows year-over-year growth while accounting for seasonality?
42. Describe a scenario where you used Power BI for predictive analysis. How did you approach the problem?

Answers

General Power BI Concepts

1. Difference between Power BI Desktop, Power BI Service, and Power BI Report Server:

   • Power BI Desktop is the development tool where you can build reports and dashboards on your local machine.
   • Power BI Service (cloud-based) is where you publish, share, and collaborate on reports with others in your organization.
   • Power BI Report Server is an on-premises solution for those who need to keep their data and reports on internal servers.

2. Types of filters in Power BI:

   • Visual-level filters: Apply to a single visual.
   • Page-level filters: Apply to all visuals on a report page.
   • Report-level filters: Apply across all pages in the report.

3. Data refresh types:

   • Manual Refresh: Refresh data manually on Power BI Desktop or Service.
   • Scheduled Refresh: Schedule automatic refreshes in Power BI Service.
   • DirectQuery: Data refreshes in real-time when the report is loaded.
   • Live Connection: Always connected to the source, no need for scheduled refreshes.

4. Difference between Import Mode, DirectQuery, and Live Connection:

   • Import Mode: Loads data into Power BI’s in-memory engine for faster performance.
   • DirectQuery: Keeps data in the source database, so Power BI sends queries each time you interact.
   • Live Connection: Used for live analysis of models in tools like SSAS.

5. Row-Level Security (RLS):

   • Use Manage roles in Power BI Desktop, create roles with DAX filters, publish to Power BI Service, and assign users to these roles.

6. Handling large datasets in Power BI:

   • Use aggregation tables, data reduction techniques (filters, removing unnecessary columns), and optimized data modeling (star schema).
   • DirectQuery can help if the dataset is too large for Import Mode.

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Data Modeling and DAX

7. Importance of star schema in Power BI:

   • Star schema simplifies relationships and enables faster performance. Snowflake schema may be used if normalization is critical, though it's generally slower for querying.

8. Calculated column vs. measure:

   • Calculated columns are computed row by row when the data model loads, used in row-level filtering or grouping.
   • Measures are calculations done on the fly, typically in the context of a visual.

9. Common DAX functions - FILTER, CALCULATE, RELATED:

   • FILTER: Filters a table based on a condition.

     DAX
     FILTER(Sales, Sales[Region] = "North America")

   • CALCULATE: Changes context for calculation.

     DAX
     CALCULATE(SUM(Sales[Amount]), FILTER(Sales, Sales[Region] = "North America"))
     

   • RELATED: Retrieves values from related tables.

     DAX
     RELATED(Products[ProductName])
     

10. Running total DAX formula:

    RunningTotal = 
    CALCULATE(
        SUM(Sales[Amount]),
        FILTER(
            ALL(Sales),
            Sales[Date] <= MAX(Sales[Date])
        )
    )
    

11. Context transition in DAX:

    • When a row context (filtering by row) is transformed into a filter context (filtering by table), typically with CALCULATE or CALCULATETABLE.

12. Using ALL and ALLEXCEPT:

    • ALL removes all filters in a table.

    DAX
    TotalSales = CALCULATE(SUM(Sales[Amount]), ALL(Sales))
    

    • ALLEXCEPT removes all filters except specified columns.

    DAX
    CALCULATE(SUM(Sales[Amount]), ALLEXCEPT(Sales, Sales[Product]))

13. Date and time calculations:

    • Common functions include TOTALYTD, TOTALQTD, and SAMEPERIODLASTYEAR to manage time-based calculations.

    DAX
    SalesLastYear = CALCULATE(SUM(Sales[Amount]), SAMEPERIODLASTYEAR(Dates[Date]))

---

Advanced Power BI Visualizations and Reporting

14. Creating drill-through or drill-down:

    • Drill-down: Enable data hierarchy in visuals.
    • Drill-through: Create drill-through pages with specific filter conditions.

15. Dynamic titles based on slicers:

    • Use DAX to create a measure for the title, then set the title as dynamic text:

    DAX
    Title = "Sales Report for " & SELECTEDVALUE(Regions[Region])
    

16. User interactions (bookmarks, tooltips):

    • Bookmarks: Capture report states for navigation.
    • Tooltips: Customize visual tooltips to display additional information.

17. Conditional formatting examples:

    • Format based on value ranges or conditions using fields or DAX measures.

18. Bookmarks for storytelling:

    • Create bookmarks for different stages of analysis and toggle them with buttons for a story-like flow.

19. Display only selected values without dimming:

    • Use custom visuals or conditional formatting to control visibility.

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Power BI Performance Optimization

20. Performance optimization best practices:

    • Use aggregations, remove unused columns, reduce model complexity, and optimize DAX.

21. Optimize model with millions of rows:

    • Use incremental refresh, DirectQuery for real-time, and aggregated tables to reduce row counts.

22. Composite models:

    • Allows combining DirectQuery and Import modes, optimizing for real-time and historical data.

23. Query folding:

    • Ensures transformations happen at the source rather than in Power BI, improving speed and efficiency.

24. Setting up aggregations:

    • Set aggregated tables at coarser grain (e.g., monthly instead of daily) for improved performance.

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Power BI and Integration with Other Tools

25. External database integration:

    • Use Power BI connectors and configure gateway connections for on-premises sources.

26. Power BI with Snowflake:

    • Direct integration with Power BI connector for Snowflake, managing connectivity and refresh scheduling.

27. Publishing to SharePoint, Teams, or embedding:

    • Use Power BI embed options or the Power BI web part for SharePoint and Teams.

28. Version control for Power BI:

    • Use OneDrive or SharePoint to manage report versions, and use documentation for version tracking.

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Scenario-Based and Troubleshooting

29. Complex report/dashboard example:

    • Describe a project with custom visuals, interactive elements, and data modeling challenges.

30. Troubleshooting performance issues:

    • Use Performance Analyzer, DAX Studio, and optimize query folding and model design.

31. Unexpected results in reports:

    • Validate DAX logic, check relationships, and ensure no unintended filter context.

32. Different data for different users:

    • Implement Row-Level Security with user-based filtering.

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Security and Deployment

33. Different security implementations:

    • RLS, OLS (Object-Level Security), and Data Classification in Power BI Service.

34. RLS for multiple tables:

    • Apply DAX filters across relationships or use bridge tables for security filtering.

35. Data governance and security:

    • Use RLS, secure data gateways, and data encryption for secure sharing.

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Power BI in Real-World Projects

36. Project example with impact on business:

    • Describe how the insights from the Power BI report led to data-driven decisions.

37. Approaching a dashboard for non-data users:

    • Use simplified visuals and clear labels, and avoid technical terms.

38. Microfinance or banking project example:

    • Discuss the dashboards you created, such as those for tracking approvals and loan statuses.

39. Creating a scalable Power BI environment:

    • Focus on Power BI Premium, incremental refresh, and workspaces for organization-wide access.

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Analytical and Problem-Solving Scenarios

40. Calculating MRR or Retention Rate:

    • Use DAX to calculate recurring revenue and retention based on customer data.

41. Year-over-year growth with seasonality:

    • Use SAMEPERIODLASTYEAR and other time-intelligence functions.

42. Predictive analysis:

    • Describe use cases with Machine Learning or Python/R integration for advanced predictive analysis.