Power BI Interview Questions: Basics, DAX, Modeling, Visualization & Optimization Tips

Power BI Interview Questions: Mastering Basics, DAX, Data Modeling & Visualization

Power BI has become the go-to tool for data visualization and business intelligence. Whether you are an aspiring Data Analyst or a professional preparing for a Power BI interview, mastering the essentials, DAX, Power Query, and visualization best practices is crucial. Over my years as a Power BI tutor and freelance consultant, I’ve helped many professionals crack interviews and optimize real-world dashboards. Today, I’m sharing a comprehensive guide to the most commonly asked Power BI interview questions.

Table of Contents

• Power BI Basics
• Data Modeling
• DAX (Data Analysis Expressions)
• Power Query & Data Transformation
• Data Visualization
• Performance Optimization
• SQL & Database Integration
• Scenario-Based Questions

Power BI Basics

1. Key Features of Power BI: Interactive dashboards, real-time analytics, AI-powered insights, and easy integration with multiple data sources.
2. Power BI Desktop vs Service vs Mobile: Desktop is for report creation, Service is cloud-based sharing, Mobile allows on-the-go dashboards. “Jahan jarurat wahan Power BI!”
3. Data Connectivity Modes: Import, DirectQuery, and Live Connection—choose based on dataset size and update frequency.
4. Row-Level Security (RLS): Restrict data access by roles. Example: Regional sales managers can see only their region's sales.

Data Modeling

5. Creating Relationships: Use primary and foreign keys; enforce cardinality (one-to-many) for clarity.
6. Star vs Snowflake Schema: Star is denormalized for simplicity, Snowflake is normalized for large datasets. “Samajh ke hi use karo schema!”
7. Surrogate Keys: Simplify joins and improve performance; avoid composite natural keys.
8. Circular Relationships: Break loops by creating bridge tables or using DAX measures carefully.
9. Calculated Columns vs Measures: Columns are row-level, measures aggregate dynamically. Use measures for performance.

DAX (Data Analysis Expressions)

10. YTD Sales: CALCULATE(SUM(Sales[Amount]), DATESYTD(Calendar[Date]))
11. SUM vs SUMX vs CALCULATE: SUM aggregates a column, SUMX iterates a table, CALCULATE modifies filter context.
12. FILTER Function: Example: CALCULATE(SUM(Sales[Amount]), FILTER(Sales, Sales[Region]="East"))
13. Context in DAX: Row context (per row), Filter context (per selection). Crucial for accurate calculations.
14. Running Total: CALCULATE(SUM(Sales[Amount]), FILTER(ALL(Calendar), Calendar[Date] <= MAX(Calendar[Date])))

Power Query & Data Transformation

15. Handling Missing/Inconsistent Data: Replace nulls, filter errors, standardize formats.
16. M Language: Formula language in Power Query for data transformations.
17. Splitting/Merging Columns: Text.Split, Merge Queries for combining datasets.
18. Appending vs Merging Queries: Append = stacking rows, Merge = joining tables horizontally.

Data Visualization

19. Choosing Visualization: Bar for comparisons, line for trends, pie for composition. Always match chart with question.
20. Slicers: Interactive filters in dashboards.
21. Bookmarks: Save report views or create storytelling dashboards.
22. Drill-Through Pages: Navigate from summary to detailed data for deeper insights.
23. Custom Visuals: Import from AppSource for enhanced reporting.

Performance Optimization

24. Optimize Reports: Reduce columns, avoid calculated columns when possible, aggregate data.
25. Reduce Report Size: Use summarized tables, remove unused columns.
26. Handling Large Datasets: DirectQuery, aggregations, or incremental refresh.
27. Use Aggregations: Pre-aggregated tables improve performance on big datasets.

SQL & Database Integration

28. SQL Queries for Power BI: Fetch clean data using SELECT, JOINs, filters.
29. Joining Multiple Tables: INNER JOIN, LEFT JOIN, RIGHT JOIN based on analysis requirement.
30. INNER vs LEFT JOIN Example: INNER = common records, LEFT = all from left table + matches.
31. Top 5 Customers by Sales: SELECT TOP 5 CustomerID, SUM(SalesAmount) AS TotalSales FROM Sales GROUP BY CustomerID ORDER BY TotalSales DESC

Scenario-Based Questions

32. Implementing RLS: Define roles for users based on departments or regions.
33. Challenging Power BI Project: Share real experience optimizing dashboard performance or complex DAX calculation.
34. Handling Real-Time Updates: Use streaming datasets or DirectQuery connections.
35. Debugging Incorrect Data: Validate queries, check relationships, audit calculations.

Pro Tip: Practice solving real datasets rather than memorizing questions. Agar aap “data se dosti” karoge, interview apne aap easy lagne lagega.

As a Power BI tutor and freelance consultant, I guide professionals to turn theory into practice, optimize dashboards, and ace interviews. Whether you are looking for tutoring, freelance projects, or full-time roles, mastering these concepts will give you a competitive edge.