Power BI Interview Questions Asked Bajaj Auto Ltd

1. Self Introduction
2. What are your roles and responsibilities of your project?
3. Difference between Import Mode and Direct Mode?
4. What kind of projects have you worked on Domain?
5. How do you handle complex data transformations in Power Query? Can you provide an example of a challenging transformation you implemented?
6. What challenges you faced while doing a projects?
7. Types of Refreshes in Power BI?
8. What is DAX in Power BI?
9. How do you perform data cleansing and transformation in Power BI?
10. How do you connect to data sources in Power BI?
11. What are the components in Power BI?
12. What is Power Pivot will do in Power BI?
13. Write a query to fetch top 5 employees having highest salary?
14. Write a query to find 2nd highest salary from employee table?
15. Difference between Rank function & Dense Rank function in SQL?
16. Difference between Power BI Desktop & Power BI Service?
17. How will you optimize Power BI reports?
18. What are the difficulties you have faced when doing a projects?
19. How can you optimize a SQL query?
20. What is Indexes?
21. How ETL process happen in Power BI?
22. What is difference between Star schema & Snowflake schema and how will know when to use which schemas respectively?
23. How will you perform filtering & it's types?
24. What is Bookmarks?
25. Difference between Drilldown and Drill through in Power BI?
26. Difference between Calculated column and measure?
27. Difference between Slicer and Filter?
28. What is a use Pandas, Matplotlib, seaborn Libraries?
29. Difference between Sum and SumX?
30. Do you have any questions?

Advanced SQL Optimization Tips for Data Analysts

Use Proper Indexing: Create indexes for frequently queried columns.

Avoid SELECT *: Specify only required columns to improve performance.

Use WHERE Instead of HAVING: Filter data early in the query.

Limit Joins: Avoid excessive joins to reduce query complexity.

Apply LIMIT or TOP: Retrieve only the required rows.

Optimize Joins: Use INNER JOIN over OUTER JOIN where applicable.

Use Temporary Tables: Break complex queries into smaller parts.

Avoid Functions on Indexed Columns: It prevents index usage.

Use CTEs for Readability: Simplify nested queries using Common Table Expressions.

Analyze Execution Plans: Identify bottlenecks and optimize queries.

Here you can find SQL Interview Resources👇
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✅ Power BI Project Ideas for Data Analysts 📊💡

Real-world projects help you stand out in job applications and interviews.

1️⃣ Sales Dashboard
• Track revenue, profit, and sales by region/product
• Add slicers for year, month, category
• Source: Sample Superstore dataset

2️⃣ HR Analytics Dashboard
• Analyze employee attrition, performance, and satisfaction
• KPIs: attrition rate, avg tenure, engagement score
• Use Excel or mock HR dataset

3️⃣ E-commerce Analysis
• Show total orders, AOV (average order value), top-selling items
• Use date filters, category breakdowns
• Optional: add customer segmentation

4️⃣ Financial Report
• Monthly expenses vs income
• Budget variance tracking
• Charts for category-wise breakdown

5️⃣ Healthcare Analytics
• Hospital admissions, treatment outcomes, patient demographics
• Drill-through: see patient-level detail by department
• Public health datasets available online

6️⃣ Marketing Campaign Tracker
• Click-through rates, conversion rates, campaign ROI
• Compare across channels (email, social, paid ads)

🧠 Bonus Tips:
• Use DAX to create measures
• Add tooltips and slicers
• Make the design clean and professional

📌 Practice Task:
Choose one topic → Get a dataset → Build a dashboard → Upload screenshots to GitHub

Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

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✅ Data Analytics Essentials

TECH SKILLS (NON-NEGOTIABLE)

1️⃣ SQL
• Joins, Group by, Window functions
• Handle NULLs and duplicates
Example: LEFT JOIN fits a churn query to include non-churned users

2️⃣ Excel
• Pivot tables, Lookups, IF logic
• Clean raw data fast
Example: Reconcile 50k rows in minutes using Pivot tables

3️⃣ Power BI or Tableau
• Data modeling, Measures, Filters
• One dashboard, One question
Example: Sales drop by region and month dashboard

4️⃣ Python
• pandas for cleaning and analysis
• matplotlib or seaborn for quick visuals
Example: Groupby revenue by cohort

5️⃣ Statistics Basics
• Mean vs median, Variance, Correlation
• Know when averages lie
Example: Median salary explains skewed data

 

SOFT SKILLS (DEAL BREAKERS)

1️⃣ Business Thinking
• Ask why before how
• Tie insights to decisions
Example: High churn points to onboarding gaps

2️⃣ Communication
• Explain insights without jargon
• One slide, One takeaway
Example: Revenue fell due to fewer repeat users

3️⃣ Problem Framing
• Convert vague asks into clear questions
• Define metrics early
Example: What defines an active user?

4️⃣ Attention to Detail
• Validate numbers
• Double check logic
• Small errors kill trust

5️⃣ Stakeholder Handling
• Listen first
• Clarify scope
• Push back with data

🎯 Balance both tech and soft skills to grow faster as an analyst

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