✅ 5 Power BI Projects for Beginners 📊🟡
1️⃣ Sales Dashboard
→ Track revenue, profit, top products & sales by region
→ Practice: bar charts, slicers, KPIs, date filters
2️⃣ Customer Analysis Report
→ Analyze customer demographics, behavior, and retention
→ Practice: pie charts, filters, clustering
3️⃣ HR Analytics Dashboard
→ Monitor employee count, attrition rate, department stats
→ Practice: cards, stacked bars, trend lines
4️⃣ Financial Statement Report
→ Visualize income, expenses, cash flow trends
→ Practice: waterfall chart, time intelligence
5️⃣ Social Media Performance Dashboard
→ Track engagement, followers, reach by platform
→ Practice: multi-page reports, custom visuals, drill-through
💡 Tip: Use sample datasets from Kaggle, Microsoft, or mock Excel files.
👍 Tap ❤️ if you found this helpful!
1️⃣ Sales Dashboard
→ Track revenue, profit, top products & sales by region
→ Practice: bar charts, slicers, KPIs, date filters
2️⃣ Customer Analysis Report
→ Analyze customer demographics, behavior, and retention
→ Practice: pie charts, filters, clustering
3️⃣ HR Analytics Dashboard
→ Monitor employee count, attrition rate, department stats
→ Practice: cards, stacked bars, trend lines
4️⃣ Financial Statement Report
→ Visualize income, expenses, cash flow trends
→ Practice: waterfall chart, time intelligence
5️⃣ Social Media Performance Dashboard
→ Track engagement, followers, reach by platform
→ Practice: multi-page reports, custom visuals, drill-through
💡 Tip: Use sample datasets from Kaggle, Microsoft, or mock Excel files.
👍 Tap ❤️ if you found this helpful!
❤3👏2
Master Power BI with this Cheat Sheet🔥
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:
✅ Use DAX for efficient data analysis.
✅ Optimize data models for performance.
✅ Utilize drill-through and drill-down for deeper insights.
✅ Leverage bookmarks for enhanced navigation.
✅ Annotate your reports with comments for clarity.
Like this post if you need more content like this 👍❤️
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:
✅ Use DAX for efficient data analysis.
✅ Optimize data models for performance.
✅ Utilize drill-through and drill-down for deeper insights.
✅ Leverage bookmarks for enhanced navigation.
✅ Annotate your reports with comments for clarity.
Like this post if you need more content like this 👍❤️
❤5🔥1
📊 Complete Power BI Syllabus Roadmap (Beginner to Expert) ✨
🔰 Beginner Level:
1. Introduction to Power BI:
• What is Power BI? Components of Power BI
• Power BI Desktop Installation and Interface Overview
2. Data Acquisition:
• Connecting to Various Data Sources (Excel, CSV, Databases, Web)
• Import vs. DirectQuery Mode
3. Power Query Editor:
• Data Transformation and Cleaning (Removing Duplicates, Handling Null Values, Changing Data Types)
• Appending and Merging Queries
• Creating Calculated Columns
4. Data Modeling:
• Creating Relationships between Tables
• Understanding Cardinality and Cross-Filter Direction
5. Basic Visualizations:
• Creating Bar Charts, Line Charts, Pie Charts, Tables, Matrices
• Using Slicers, Filters, and Tooltips
6. Publishing and Sharing:
* Publishing Reports to Power BI Service
* Creating Dashboards
* Sharing Dashboards and Reports with Others
7. Basic Projects: Building a simple Sales Dashboard, Creating a basic Customer Analysis Report
⚙️ Intermediate Level:
1. Advanced DAX:
• Understanding DAX Syntax and Functions
• Creating Measures for Aggregation, Filtering, and Time Intelligence
• Using CALCULATE, FILTER, ALL, and RELATED Functions
2. Advanced Data Modeling:
• Star Schema vs. Snowflake Schema
• Creating Calculated Tables
• Using Hierarchies
3. Advanced Visualizations:
• Creating Map Visuals, Scatter Charts, Gauge Charts, Treemaps
• Using Bookmarks and Buttons for Navigation
• Custom Visuals from AppSource
4. Power BI Service:
• Managing Datasets, Gateways, and Dataflows
• Setting up Scheduled Refreshes
• Row-Level Security (RLS)
5. Dataflows:
• Creating and Managing Dataflows
• Using Dataflows for Data Preparation and Transformation
6. Intermediate Projects: Building an HR Analytics Dashboard, Creating a Financial Statement Report
🏆 Expert Level:
1. DAX Optimization:
• Identifying and Resolving Performance Bottlenecks in DAX Code
• Using Variables and Iterators Effectively
• Understanding Storage Engine vs. Formula Engine
2. Advanced Data Modeling Techniques:
• Implementing Role-Playing Dimensions
• Handling Many-to-Many Relationships
3. Power BI Embedded:
• Embedding Power BI Reports and Dashboards in Custom Applications
• Using the Power BI REST API
4. Power BI Administration:
• Managing Power BI Tenant Settings
• Monitoring Performance and Usage
• Implementing Data Governance and Security Policies
5. Power BI Premium:
• Understanding Premium Capacity and Features
• Using Aggregations and Incremental Refresh
6. AI and Machine Learning in Power BI:
• Using the AI Visuals (Key Influencers, Decomposition Tree)
• Integrating Custom Machine Learning Models with Power BI
7. Expert Projects: Designing and implementing a comprehensive enterprise-level Power BI solution.
💡 Bonus: Learn about Power Automate Integration, Power Apps Integration, and Data Storytelling Techniques. Get familiar with new features and updates released by Microsoft regularly.
👍 Tap ❤️ for more
🔰 Beginner Level:
1. Introduction to Power BI:
• What is Power BI? Components of Power BI
• Power BI Desktop Installation and Interface Overview
2. Data Acquisition:
• Connecting to Various Data Sources (Excel, CSV, Databases, Web)
• Import vs. DirectQuery Mode
3. Power Query Editor:
• Data Transformation and Cleaning (Removing Duplicates, Handling Null Values, Changing Data Types)
• Appending and Merging Queries
• Creating Calculated Columns
4. Data Modeling:
• Creating Relationships between Tables
• Understanding Cardinality and Cross-Filter Direction
5. Basic Visualizations:
• Creating Bar Charts, Line Charts, Pie Charts, Tables, Matrices
• Using Slicers, Filters, and Tooltips
6. Publishing and Sharing:
* Publishing Reports to Power BI Service
* Creating Dashboards
* Sharing Dashboards and Reports with Others
7. Basic Projects: Building a simple Sales Dashboard, Creating a basic Customer Analysis Report
⚙️ Intermediate Level:
1. Advanced DAX:
• Understanding DAX Syntax and Functions
• Creating Measures for Aggregation, Filtering, and Time Intelligence
• Using CALCULATE, FILTER, ALL, and RELATED Functions
2. Advanced Data Modeling:
• Star Schema vs. Snowflake Schema
• Creating Calculated Tables
• Using Hierarchies
3. Advanced Visualizations:
• Creating Map Visuals, Scatter Charts, Gauge Charts, Treemaps
• Using Bookmarks and Buttons for Navigation
• Custom Visuals from AppSource
4. Power BI Service:
• Managing Datasets, Gateways, and Dataflows
• Setting up Scheduled Refreshes
• Row-Level Security (RLS)
5. Dataflows:
• Creating and Managing Dataflows
• Using Dataflows for Data Preparation and Transformation
6. Intermediate Projects: Building an HR Analytics Dashboard, Creating a Financial Statement Report
🏆 Expert Level:
1. DAX Optimization:
• Identifying and Resolving Performance Bottlenecks in DAX Code
• Using Variables and Iterators Effectively
• Understanding Storage Engine vs. Formula Engine
2. Advanced Data Modeling Techniques:
• Implementing Role-Playing Dimensions
• Handling Many-to-Many Relationships
3. Power BI Embedded:
• Embedding Power BI Reports and Dashboards in Custom Applications
• Using the Power BI REST API
4. Power BI Administration:
• Managing Power BI Tenant Settings
• Monitoring Performance and Usage
• Implementing Data Governance and Security Policies
5. Power BI Premium:
• Understanding Premium Capacity and Features
• Using Aggregations and Incremental Refresh
6. AI and Machine Learning in Power BI:
• Using the AI Visuals (Key Influencers, Decomposition Tree)
• Integrating Custom Machine Learning Models with Power BI
7. Expert Projects: Designing and implementing a comprehensive enterprise-level Power BI solution.
💡 Bonus: Learn about Power Automate Integration, Power Apps Integration, and Data Storytelling Techniques. Get familiar with new features and updates released by Microsoft regularly.
👍 Tap ❤️ for more
❤7👏1
Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
❤8🔥1
Key Power BI Functions Every Analyst Should Master
DAX Functions:
1. CALCULATE():
Purpose: Modify context or filter data for calculations.
Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")
2. SUM():
Purpose: Adds up column values.
Example: SUM(Sales[Amount])
3. AVERAGE():
Purpose: Calculates the mean of column values.
Example: AVERAGE(Sales[Amount])
4. RELATED():
Purpose: Fetch values from a related table.
Example: RELATED(Customers[Name])
5. FILTER():
Purpose: Create a subset of data for calculations.
Example: FILTER(Sales, Sales[Amount] > 100)
6. IF():
Purpose: Apply conditional logic.
Example: IF(Sales[Amount] > 1000, "High", "Low")
7. ALL():
Purpose: Removes filters to calculate totals.
Example: ALL(Sales[Region])
8. DISTINCT():
Purpose: Return unique values in a column.
Example: DISTINCT(Sales[Product])
9. RANKX():
Purpose: Rank values in a column.
Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))
10. FORMAT():
Purpose: Format numbers or dates as text.
Example: FORMAT(TODAY(), "MM/DD/YYYY")
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you want me to continue this Power BI series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
DAX Functions:
1. CALCULATE():
Purpose: Modify context or filter data for calculations.
Example: CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")
2. SUM():
Purpose: Adds up column values.
Example: SUM(Sales[Amount])
3. AVERAGE():
Purpose: Calculates the mean of column values.
Example: AVERAGE(Sales[Amount])
4. RELATED():
Purpose: Fetch values from a related table.
Example: RELATED(Customers[Name])
5. FILTER():
Purpose: Create a subset of data for calculations.
Example: FILTER(Sales, Sales[Amount] > 100)
6. IF():
Purpose: Apply conditional logic.
Example: IF(Sales[Amount] > 1000, "High", "Low")
7. ALL():
Purpose: Removes filters to calculate totals.
Example: ALL(Sales[Region])
8. DISTINCT():
Purpose: Return unique values in a column.
Example: DISTINCT(Sales[Product])
9. RANKX():
Purpose: Rank values in a column.
Example: RANKX(ALL(Sales[Region]), SUM(Sales[Amount]))
10. FORMAT():
Purpose: Format numbers or dates as text.
Example: FORMAT(TODAY(), "MM/DD/YYYY")
You can refer these Power BI Interview Resources to learn more: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you want me to continue this Power BI series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤6
✅ Power BI Roadmap: Step-by-Step Guide to Master Power BI 📊💻
Whether you're aiming to be a data analyst, business intelligence pro, or dashboard expert — this roadmap has you covered 👇
📍 1. Power BI Basics
⦁ Get familiar with Power BI Desktop interface
⦁ Connect to data sources (Excel, CSV, databases)
⦁ Learn Basic visualizations: tables, charts, slicers
📍 2. Data Transformation & Modeling
⦁ Use Power Query Editor to clean & shape data
⦁ Create relationships between tables
⦁ Understand data types & formats
📍 3. DAX Fundamentals
⦁ Master calculated columns & measures
⦁ Learn core functions: SUM, CALCULATE, FILTER, RELATED
⦁ Use variables and time intelligence functions
📍 4. Advanced Visualizations
⦁ Build interactive reports and dashboards
⦁ Use bookmarks, buttons & drill-throughs
⦁ Customize visuals & layouts for storytelling
📍 5. Data Refresh & Gateway
⦁ Set up scheduled refresh with data gateways
⦁ Understand live vs import modes
⦁ Manage refresh performance
📍 6. Row-Level Security (RLS)
⦁ Learn to restrict data access by user roles
⦁ Implement roles & test security in reports
📍 7. Power BI Service & Collaboration
⦁ Publish reports to Power BI Service
⦁ Share dashboards and collaborate with teams
⦁ Use workspaces, apps, and permissions
📍 8. Power BI Mobile & Embedded
⦁ Optimize reports for mobile devices
⦁ Embed Power BI visuals in apps or websites
📍 9. Performance Optimization
⦁ Use Performance Analyzer to tune reports
⦁ Optimize data models & DAX queries
⦁ Best practices for large datasets
📍 10. Power BI API & Automation
⦁ Use Power BI REST API for automation
⦁ Integrate with Power Automate & Azure services
📍 11. Real Projects & Practice
⦁ Build sample dashboards: Sales, Marketing, Finance
⦁ Join challenges on platforms like Enterprise DNA, Radacad
📍 12. Certification & Career Growth
⦁ Prepare for DA-100 / PL-300 certification
⦁ Build portfolio & LinkedIn presence
⦁ Apply for BI Analyst & Power BI Developer roles
💡 Pro Tip: Combine Power BI skills with SQL and Python for a powerful data career combo!
💬 Double Tap ♥️ For More!
Whether you're aiming to be a data analyst, business intelligence pro, or dashboard expert — this roadmap has you covered 👇
📍 1. Power BI Basics
⦁ Get familiar with Power BI Desktop interface
⦁ Connect to data sources (Excel, CSV, databases)
⦁ Learn Basic visualizations: tables, charts, slicers
📍 2. Data Transformation & Modeling
⦁ Use Power Query Editor to clean & shape data
⦁ Create relationships between tables
⦁ Understand data types & formats
📍 3. DAX Fundamentals
⦁ Master calculated columns & measures
⦁ Learn core functions: SUM, CALCULATE, FILTER, RELATED
⦁ Use variables and time intelligence functions
📍 4. Advanced Visualizations
⦁ Build interactive reports and dashboards
⦁ Use bookmarks, buttons & drill-throughs
⦁ Customize visuals & layouts for storytelling
📍 5. Data Refresh & Gateway
⦁ Set up scheduled refresh with data gateways
⦁ Understand live vs import modes
⦁ Manage refresh performance
📍 6. Row-Level Security (RLS)
⦁ Learn to restrict data access by user roles
⦁ Implement roles & test security in reports
📍 7. Power BI Service & Collaboration
⦁ Publish reports to Power BI Service
⦁ Share dashboards and collaborate with teams
⦁ Use workspaces, apps, and permissions
📍 8. Power BI Mobile & Embedded
⦁ Optimize reports for mobile devices
⦁ Embed Power BI visuals in apps or websites
📍 9. Performance Optimization
⦁ Use Performance Analyzer to tune reports
⦁ Optimize data models & DAX queries
⦁ Best practices for large datasets
📍 10. Power BI API & Automation
⦁ Use Power BI REST API for automation
⦁ Integrate with Power Automate & Azure services
📍 11. Real Projects & Practice
⦁ Build sample dashboards: Sales, Marketing, Finance
⦁ Join challenges on platforms like Enterprise DNA, Radacad
📍 12. Certification & Career Growth
⦁ Prepare for DA-100 / PL-300 certification
⦁ Build portfolio & LinkedIn presence
⦁ Apply for BI Analyst & Power BI Developer roles
💡 Pro Tip: Combine Power BI skills with SQL and Python for a powerful data career combo!
💬 Double Tap ♥️ For More!
❤9
✅ Power BI Scenario-Based Questions 📊⚡
🧮 Scenario 1: Measure vs. Calculated Column
Question: You need to create a new column to categorize sales as “High” or “Low” based on a threshold. Would you use a calculated column or a measure? Why?
Answer: I would use a calculated column because the categorization is row-level logic and needs to be stored in the data model for filtering and visual grouping. Measures are better suited for aggregations and calculations on summarized data.
🔁 Scenario 2: Handling Data from Multiple Sources
Question: How would you combine data from Excel, SQL Server, and a web API into a single Power BI report?
Answer: I’d use Power Query to connect to each data source and perform necessary transformations. Then, I’d establish relationships in the data model using the Manage Relationships pane. I’d ensure consistent data types and structure before building visuals that integrate insights across all sources.
🔐 Scenario 3: Row-Level Security
Question: How would you ensure that different departments only see data relevant to them in a Power BI report?
×Answer:× I’d implement ×Row-Level Security (RLS)× by defining roles in Power BI Desktop using DAX filters (e.g., [Department] = USERNAME()), then publish the report to the Power BI Service and assign users to the appropriate roles.
📉 Scenario 4: Reducing Dataset Size
Question: Your Power BI model is too large and hitting performance limits. What would you do?
Answer: I’d remove unused columns, reduce granularity where possible, and switch to star schema modeling. I might also aggregate large tables, optimize DAX, and disable auto date/time features to save space.
📌 Tap ❤️ for more!
🧮 Scenario 1: Measure vs. Calculated Column
Question: You need to create a new column to categorize sales as “High” or “Low” based on a threshold. Would you use a calculated column or a measure? Why?
Answer: I would use a calculated column because the categorization is row-level logic and needs to be stored in the data model for filtering and visual grouping. Measures are better suited for aggregations and calculations on summarized data.
🔁 Scenario 2: Handling Data from Multiple Sources
Question: How would you combine data from Excel, SQL Server, and a web API into a single Power BI report?
Answer: I’d use Power Query to connect to each data source and perform necessary transformations. Then, I’d establish relationships in the data model using the Manage Relationships pane. I’d ensure consistent data types and structure before building visuals that integrate insights across all sources.
🔐 Scenario 3: Row-Level Security
Question: How would you ensure that different departments only see data relevant to them in a Power BI report?
×Answer:× I’d implement ×Row-Level Security (RLS)× by defining roles in Power BI Desktop using DAX filters (e.g., [Department] = USERNAME()), then publish the report to the Power BI Service and assign users to the appropriate roles.
📉 Scenario 4: Reducing Dataset Size
Question: Your Power BI model is too large and hitting performance limits. What would you do?
Answer: I’d remove unused columns, reduce granularity where possible, and switch to star schema modeling. I might also aggregate large tables, optimize DAX, and disable auto date/time features to save space.
📌 Tap ❤️ for more!
❤16🔥1
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?
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?
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📊 Complete Roadmap to Become a Power BI Expert
📂 1. Understand Basics of Data & BI
– What is Business Intelligence?
– Importance of data visualization
📂 2. Learn Power BI Interface
– Power BI Desktop overview
– Power Query Editor basics
📂 3. Connect to Data Sources
– Excel, SQL Server, SharePoint, APIs, CSV, etc.
📂 4. Data Transformation & Cleaning
– Use Power Query to shape, clean, and prepare data
📂 5. Learn Data Modeling
– Create relationships between tables
– Understand star schema & normalization basics
📂 6. Master DAX (Data Analysis Expressions)
– Calculated columns, measures, time intelligence functions
📂 7. Create Interactive Visualizations
– Charts, slicers, maps, tables, and custom visuals
📂 8. Build Dashboards & Reports
– Combine visuals for insightful dashboards
– Use bookmarks, drill-throughs, tooltips
📂 9. Publish & Share Reports
– Power BI Service basics
– Sharing, workspaces, and app creation
📂 10. Learn Power BI Administration
– Row-level security (RLS)
– Gateway setup & scheduled refresh
📂 11. Practice Real-World Projects
– Sales dashboards, financial reports, customer insights
👍 Like for more!
📂 1. Understand Basics of Data & BI
– What is Business Intelligence?
– Importance of data visualization
📂 2. Learn Power BI Interface
– Power BI Desktop overview
– Power Query Editor basics
📂 3. Connect to Data Sources
– Excel, SQL Server, SharePoint, APIs, CSV, etc.
📂 4. Data Transformation & Cleaning
– Use Power Query to shape, clean, and prepare data
📂 5. Learn Data Modeling
– Create relationships between tables
– Understand star schema & normalization basics
📂 6. Master DAX (Data Analysis Expressions)
– Calculated columns, measures, time intelligence functions
📂 7. Create Interactive Visualizations
– Charts, slicers, maps, tables, and custom visuals
📂 8. Build Dashboards & Reports
– Combine visuals for insightful dashboards
– Use bookmarks, drill-throughs, tooltips
📂 9. Publish & Share Reports
– Power BI Service basics
– Sharing, workspaces, and app creation
📂 10. Learn Power BI Administration
– Row-level security (RLS)
– Gateway setup & scheduled refresh
📂 11. Practice Real-World Projects
– Sales dashboards, financial reports, customer insights
👍 Like for more!
👍6❤3
🚀 AI Journey Contest 2025: Test your AI skills!
Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!
Choose your track:
· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.
· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.
· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.
Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.
How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.
🚀 Ready for a challenge? Join a global developer community and show your AI skills!
Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!
Choose your track:
· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.
· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.
· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.
Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.
How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.
🚀 Ready for a challenge? Join a global developer community and show your AI skills!
❤4👏2
✅ 🚀 Power BI Interview Questions (For Analyst/BI Roles)
1️⃣ Explain DAX CALCULATE() Function
Used to modify the filter context of a measure.
✅ Example:
2️⃣ What is ALL() function in DAX?
Removes filters — useful for calculating totals regardless of filters.
3️⃣ How does FILTER() differ from CALCULATE()?
FILTER returns a table; CALCULATE modifies context using that table.
4️⃣ Difference between SUMX and SUM?
SUMX iterates over rows, applying an expression; SUM just totals a column.
5️⃣ Explain STAR vs SNOWFLAKE Schema
- Star: denormalized, simple
- Snowflake: normalized, complex relationships
6️⃣ What is a Composite Model?
Allows combining Import + DirectQuery sources in one report.
7️⃣ What are Virtual Tables in DAX?
Tables created in memory during calculation — not physical.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
Used for dynamic RLS.
- USERNAME(): Local machine login
- USERPRINCIPALNAME(): Cloud identity (email)
9️⃣ Explain Time Intelligence Functions
Examples:
-
Used for date-based calculations.
🔟 Common DAX Optimization Tips
- Avoid complex nested functions
- Use variables (VAR)
- Reduce row context with calculated columns
1️⃣1️⃣ What is Incremental Refresh?
Only refreshes new/changed data – improves performance in large datasets.
1️⃣2️⃣ What are Parameters in Power BI?
User-defined inputs to make reports dynamic and reusable.
1️⃣3️⃣ What is a Dataflow?
Reusable ETL layer in Power BI Service using Power Query Online.
1️⃣4️⃣ Difference Between Live Connection vs DirectQuery vs Import
- Import: Fast, offline
- DirectQuery: Real-time, slower
- Live Connection: Full model lives on SSAS
1️⃣5️⃣ Advanced Visuals Use Cases
- Decomposition Tree for root cause analysis
- KPI Cards for performance metrics
- Paginated Reports for printable tables
👍 Tap for more!
1️⃣ Explain DAX CALCULATE() Function
Used to modify the filter context of a measure.
✅ Example:
CALCULATE(SUM(Sales[Amount]), Region = "West")2️⃣ What is ALL() function in DAX?
Removes filters — useful for calculating totals regardless of filters.
3️⃣ How does FILTER() differ from CALCULATE()?
FILTER returns a table; CALCULATE modifies context using that table.
4️⃣ Difference between SUMX and SUM?
SUMX iterates over rows, applying an expression; SUM just totals a column.
5️⃣ Explain STAR vs SNOWFLAKE Schema
- Star: denormalized, simple
- Snowflake: normalized, complex relationships
6️⃣ What is a Composite Model?
Allows combining Import + DirectQuery sources in one report.
7️⃣ What are Virtual Tables in DAX?
Tables created in memory during calculation — not physical.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
Used for dynamic RLS.
- USERNAME(): Local machine login
- USERPRINCIPALNAME(): Cloud identity (email)
9️⃣ Explain Time Intelligence Functions
Examples:
-
TOTALYTD(), DATESINPERIOD(), SAMEPERIODLASTYEAR()Used for date-based calculations.
🔟 Common DAX Optimization Tips
- Avoid complex nested functions
- Use variables (VAR)
- Reduce row context with calculated columns
1️⃣1️⃣ What is Incremental Refresh?
Only refreshes new/changed data – improves performance in large datasets.
1️⃣2️⃣ What are Parameters in Power BI?
User-defined inputs to make reports dynamic and reusable.
1️⃣3️⃣ What is a Dataflow?
Reusable ETL layer in Power BI Service using Power Query Online.
1️⃣4️⃣ Difference Between Live Connection vs DirectQuery vs Import
- Import: Fast, offline
- DirectQuery: Real-time, slower
- Live Connection: Full model lives on SSAS
1️⃣5️⃣ Advanced Visuals Use Cases
- Decomposition Tree for root cause analysis
- KPI Cards for performance metrics
- Paginated Reports for printable tables
👍 Tap for more!
❤6
Most Asked SQL Interview Questions at MAANG Companies🔥🔥
Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE & HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you calculate average, sum, minimum & maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/mysqldata
Like this post if you need more 👍❤️
Hope it helps :)
Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE & HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you calculate average, sum, minimum & maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/mysqldata
Like this post if you need more 👍❤️
Hope it helps :)
❤9
🔍 Advanced Power BI Interview Questions & Answers
1️⃣ What is Power BI Aggregations?
Aggregations improve performance by precomputing data at a higher level and storing it in memory. Power BI can automatically redirect queries to aggregated tables when possible.
2️⃣ Explain the concept of Composite Models.
Composite models allow combining Import and DirectQuery data sources in a single report, offering flexibility in performance and real-time access.
3️⃣ What is the difference between Power Query and Power Pivot?
⦁ Power Query: Used for data transformation and loading
⦁ Power Pivot: Used for data modeling and DAX calculations
4️⃣ What is the role of Tabular Model in Power BI?
Power BI uses the Tabular Model (based on SSAS) for in-memory analytics, enabling fast calculations and relationships.
5️⃣ How does Incremental Refresh work?
Incremental Refresh loads only new or changed data during scheduled refreshes, improving efficiency for large datasets.
6️⃣ What is the significance of the VertiPaq engine?
VertiPaq is the in-memory engine that compresses and stores data efficiently, enabling fast query performance in Power BI.
7️⃣ How do you implement dynamic noscripts in Power BI?
Use DAX measures and card visuals to create noscripts that change based on slicer selections or filters.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
⦁ USERNAME() returns the domain\username format
⦁ USERPRINCIPALNAME() returns the email format, preferred for cloud-based RLS
9️⃣ How do you handle circular dependency errors in DAX?
Avoid creating calculated columns/measures that reference each other recursively. Use variables and restructure logic to break the loop.
🔟 What is the use of CALCULATE in DAX?
CALCULATE modifies the context of a calculation by applying filters. It’s essential for dynamic aggregations.
Example:
1️⃣1️⃣ What are Aggregation Tables and when should you use them?
Aggregation tables store pre-summarized data to improve performance on large datasets. Use them when querying detailed data is too slow.
1️⃣2️⃣ How do you implement Role-Level Security (RLS) with dynamic filters?
Create a user table with email addresses and region mappings, then use DAX with USERPRINCIPALNAME() to filter data dynamically.
1️⃣3️⃣ What is the difference between SUM and SUMX in DAX?
⦁ SUM: Adds values from a column
⦁ SUMX: Iterates over a table and evaluates an expression row by row
1️⃣4️⃣ What are Parameters in Power BI and how are they used?
Parameters allow dynamic input into queries or filters. Useful for what-if analysis, dynamic data sources, or user-driven filtering.
1️⃣5️⃣ How do you use Field Parameters in Power BI?
Field Parameters let users dynamically switch dimensions or measures in visuals using slicers—great for interactive dashboards.
1️⃣6️⃣ What is the purpose of the Performance Analyzer in Power BI?
It helps identify slow visuals and DAX queries by showing render times, query durations, and bottlenecks.
1️⃣7️⃣ How do you handle many-to-many relationships in Power BI?
Use a bridge table with unique keys and set relationships as “many-to-one” on both sides, or use DAX functions like TREATAS().
1️⃣8️⃣ What is the difference between SELECTEDVALUE and VALUES in DAX?
⦁ SELECTEDVALUE: Returns a single value if only one is selected, otherwise returns blank or a default
⦁ VALUES: Returns a table of distinct values
1️⃣9️⃣ How do you create a paginated report in Power BI?
Use Power BI Report Builder to design pixel-perfect reports ideal for printing or exporting, especially with large tables.
2️⃣0️⃣ What are the limitations of DirectQuery mode?
⦁ Slower performance due to live queries
⦁ Limited DAX functions
⦁ No support for certain transformations
⦁ Dependency on source system availability
Double Tap ❤️ For More
1️⃣ What is Power BI Aggregations?
Aggregations improve performance by precomputing data at a higher level and storing it in memory. Power BI can automatically redirect queries to aggregated tables when possible.
2️⃣ Explain the concept of Composite Models.
Composite models allow combining Import and DirectQuery data sources in a single report, offering flexibility in performance and real-time access.
3️⃣ What is the difference between Power Query and Power Pivot?
⦁ Power Query: Used for data transformation and loading
⦁ Power Pivot: Used for data modeling and DAX calculations
4️⃣ What is the role of Tabular Model in Power BI?
Power BI uses the Tabular Model (based on SSAS) for in-memory analytics, enabling fast calculations and relationships.
5️⃣ How does Incremental Refresh work?
Incremental Refresh loads only new or changed data during scheduled refreshes, improving efficiency for large datasets.
6️⃣ What is the significance of the VertiPaq engine?
VertiPaq is the in-memory engine that compresses and stores data efficiently, enabling fast query performance in Power BI.
7️⃣ How do you implement dynamic noscripts in Power BI?
Use DAX measures and card visuals to create noscripts that change based on slicer selections or filters.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
⦁ USERNAME() returns the domain\username format
⦁ USERPRINCIPALNAME() returns the email format, preferred for cloud-based RLS
9️⃣ How do you handle circular dependency errors in DAX?
Avoid creating calculated columns/measures that reference each other recursively. Use variables and restructure logic to break the loop.
🔟 What is the use of CALCULATE in DAX?
CALCULATE modifies the context of a calculation by applying filters. It’s essential for dynamic aggregations.
Example:
Sales West = CALCULATE(SUM(Sales[Amount]), Region = "West")1️⃣1️⃣ What are Aggregation Tables and when should you use them?
Aggregation tables store pre-summarized data to improve performance on large datasets. Use them when querying detailed data is too slow.
1️⃣2️⃣ How do you implement Role-Level Security (RLS) with dynamic filters?
Create a user table with email addresses and region mappings, then use DAX with USERPRINCIPALNAME() to filter data dynamically.
1️⃣3️⃣ What is the difference between SUM and SUMX in DAX?
⦁ SUM: Adds values from a column
⦁ SUMX: Iterates over a table and evaluates an expression row by row
1️⃣4️⃣ What are Parameters in Power BI and how are they used?
Parameters allow dynamic input into queries or filters. Useful for what-if analysis, dynamic data sources, or user-driven filtering.
1️⃣5️⃣ How do you use Field Parameters in Power BI?
Field Parameters let users dynamically switch dimensions or measures in visuals using slicers—great for interactive dashboards.
1️⃣6️⃣ What is the purpose of the Performance Analyzer in Power BI?
It helps identify slow visuals and DAX queries by showing render times, query durations, and bottlenecks.
1️⃣7️⃣ How do you handle many-to-many relationships in Power BI?
Use a bridge table with unique keys and set relationships as “many-to-one” on both sides, or use DAX functions like TREATAS().
1️⃣8️⃣ What is the difference between SELECTEDVALUE and VALUES in DAX?
⦁ SELECTEDVALUE: Returns a single value if only one is selected, otherwise returns blank or a default
⦁ VALUES: Returns a table of distinct values
1️⃣9️⃣ How do you create a paginated report in Power BI?
Use Power BI Report Builder to design pixel-perfect reports ideal for printing or exporting, especially with large tables.
2️⃣0️⃣ What are the limitations of DirectQuery mode?
⦁ Slower performance due to live queries
⦁ Limited DAX functions
⦁ No support for certain transformations
⦁ Dependency on source system availability
Double Tap ❤️ For More
❤8