62 Must-Know Power BI Interview Questions and Answers For Freshers and Professionals in 2025

Businesses across industries are seeking skilled Power BI professionals to manage complex data workflows, create efficient reports, and drive business performances. As a result, the demand for experts who can leverage Power BI’s capabilities—such as DAX formulas, Power Query, and data modeling—has surged.  

This blog aims to prepare you for interview questions on Power BI, tailored to different experience levels, to help you succeed in your next interview.

Fundamental Power BI Interview Questions and Answers for Beginners

Beginner roles often involve data preparation, creating basic reports, and assisting in building dashboards. You’ll work closely with senior analysts to understand data requirements and support ongoing data projects. 

To prepare, focus on mastering the Power BI Desktop interface, practicing creating and publishing reports, and understanding basic data transformations. Additionally, getting hands-on experience with sample datasets will help you build confidence.

You'll also need to be familiar with DAX basics for creating calculated columns and simple measures, as well as using Power Query for data cleansing.

Let’s explore some of the basic Power BI interview questions and answers.

1. How would you define Power BI?

A:  Power BI is a business analytics service by Microsoft that allows users to visualize and share insights from their data. It connects to a wide range of data sources, transforms the data, and creates interactive data visualizations and reports for decision-making.

Here are some of its key features:

• Power BI is used to analyze data and provide insights to business users for better decision-making.
• It connects to numerous data sources, including cloud services, databases, spreadsheets, and APIs.
• Power BI includes Power Query, which allows users to clean and reshape data before visualizing it.
• It enables the creation of interactive dashboards and reports that present data in an easy-to-understand format, such as graphs, charts, and maps.
• Power BI Service allows users to publish, share, and collaborate on reports across the organization.
• Power BI also offers features like DAX (Data Analysis Expressions) for creating custom calculations and RLS (Row-Level Security) for ensuring data security at a granular level.

Also Read: Step-by-Step Guide to Building a Career as a Power BI Developer

2. Why is Power BI widely used?

A: Power BI is widely used due to its user-friendly interface, powerful data visualization capabilities, and seamless integration with various data sources. It enables businesses to convert raw data into actionable insights through interactive dashboards and reports.

Here are a few reasons why it’s so popular:

• Power BI has a drag-and-drop interface that makes it accessible to both technical and non-technical users. While it requires minimal to no coding for basic tasks, users may still need to use DAX and Power Query for more advanced data manipulation and customization.
• It supports a wide range of data connectors, enabling users to pull data from diverse sources such as Excel, SQL databases, cloud platforms, and APIs, making it a versatile tool for different types of businesses.
• Power BI offers various chart types, graphs, and interactive features like slicers, drill-through, and tooltips, allowing users to create visually appealing and insightful reports.
• Power BI comes with a free version with significant features, and its paid plans are reasonably priced compared to other BI tools, making it accessible for businesses of all sizes.
• Power BI Service allows users to share reports and collaborate on them in real time. The integration with other Microsoft services like Excel and Teams adds further value for businesses using Microsoft products.
• Power BI incorporates advanced features like DAX for custom calculations, Power Query for data transformation, and Power BI Embedded for embedding reports in applications, catering to both entry-level analysts and power users.

Also Read: Data Visualisation: The What, The Why, and The How!

3. What sets Power BI apart from Tableau?

A: Power BI is ideal for businesses using Microsoft tools, offering an affordable, easy-to-use solution for data visualization and reporting on medium-sized datasets.

Tableau is preferred for complex, large datasets and advanced visualizations, making it ideal for professional data analysts needing more customization and flexibility.

Here’s a table highlighting key differences that set them apart:

4. How do Power Query and Power Pivot differ?

A:  Power Query and Power Pivot are both tools within Power BI and Excel used for data manipulation, but they serve different purposes. 

Here’s a table differentiating them:

5. What is the function of Power BI Desktop?

A: Power BI Desktop is a free application that enables users to connect to various data sources, transform data, and create interactive reports and visualizations. It allows businesses to analyze their data locally before sharing it on the Power BI service for collaboration.

For example, a retail company can use Power BI Desktop to connect to sales data from multiple stores, clean and aggregate the data, then create interactive dashboards showing sales performance, trends, and insights. This report can then be shared with management for data-driven decision-making.

6. What is the Power Pivot used for?

A: Power Pivot is a data modeling tool in Excel and Power BI used for building complex data models. It allows users to create relationships between tables, define calculated columns, and develop powerful measures using DAX (Data Analysis Expressions). 

Power Pivot is primarily used for handling large datasets and performing advanced data analysis, beyond what traditional Excel formulas can manage.

For example, in a financial company, Power Pivot can be used to combine sales, expenses, and budget data from different departments into one model. It can then create complex calculations like profit margins or year-over-year growth, enabling management to analyze and make data-driven decisions efficiently.

7. What role does Power Query play in Power BI?

A: Power Query allows users to extract, transform, and load (ETL) data from various sources into the Power BI model. It allows users to clean, reshape, and combine data before loading it into Power BI for analysis and reporting.

For instance, a marketing team could use Power Query to pull data from multiple sources like Google Analytics, social media platforms, and Excel files, clean the data (removing duplicates, handling missing values), and merge them into a unified dataset. 

This prepared data can then be used to build visualizations and reports that track campaign performance across various channels.

Also Read: Data Cleaning Techniques: Learn Simple & Effective Ways To Clean Data

8. What are the key components of Microsoft’s self-service BI platform?

A: Microsoft's self-service Business Intelligence (BI) platform consists of several key components that work together to enable users to easily access, analyze, and visualize data without needing extensive technical expertise. These components include:

• Power BI Desktop: A free, Windows-based application that allows users to connect to data sources, transform data, and create interactive reports and dashboards.
• Power BI Service: A cloud-based platform where users can publish, share, and collaborate on Power BI reports and dashboards. It supports sharing data insights with team members or the entire organization.
• Power Query: A tool for data extraction and transformation, allowing users to clean, reshape, and prepare data from various sources before importing it into Power BI.
• Power Pivot: A tool used for data modeling, creating relationships between tables, and defining advanced calculations using DAX (Data Analysis Expressions).
• Power View: An interactive data visualization tool that enables users to create charts, graphs, and reports to visualize data insights.
• Power Q&A: A feature that allows users to ask natural language questions about their data and receive immediate insights in the form of reports or visualizations.

Together, these tools enable users to gather data, transform it into useful information, and share insights in a collaborative, user-friendly environment.

9. How would you explain self-service BI?

A: Self-service BI (Business Intelligence) refers to the ability for end-users, typically business analysts or even non-technical users, to access, analyze, and visualize their own data without relying heavily on IT or specialized data teams. It empowers users to perform data analysis, create reports, and build dashboards on their own, using user-friendly tools like Power BI, Tableau, or Qlik.

For example, a sales manager can use self-service BI tools to directly access sales data, create performance dashboards, and analyze trends without needing a developer to write complex queries or reports. This reduces dependency on IT, speeds up decision-making, and allows business users to derive actionable insights independently.

10. What is DAX, and what is its significance?

A: DAX (Data Analysis Expressions) is a powerful formula language used in Power BI, Power Pivot, and SQL Server Analysis Services (SSAS) to create custom calculations and expressions. 

It is primarily used for defining calculated columns, measures, and custom tables in data models, enabling users to perform complex data analysis beyond basic aggregation.

DAX is significant because it allows users to:

• Create complex calculations: You can perform advanced mathematical and statistical operations like time intelligence functions (e.g., year-to-date, moving averages), conditional logic, and aggregations.
• Enhance data models: DAX helps create new insights by defining relationships between tables, filtering data dynamically, and performing calculations across different levels of granularity.
• Optimize performance: DAX formulas are designed to be efficient and perform calculations quickly, making large datasets manageable in Power BI.

For example, a financial analyst can use DAX to calculate "Total Sales YTD" (Year-To-Date) by using time intelligence functions, allowing them to track sales performance in real-time and compare it against the target.

11. What are Filters in Power BI, and how do they work?

A: Filters in Power BI are tools used to restrict the data displayed in reports and visualizations based on specific criteria. They help focus on relevant data, enhance user experience, and allow users to drill down into particular segments of the data.

Filters can be applied at different levels, such as the visual, page, or report level, and can be used with various types of data (e.g., text, numbers, dates).

Here’s the different types and how they work:

• Visual-level filters apply to individual visualizations (charts, tables, etc.), affecting only the data shown in that specific visualization. For example, you can filter a bar chart to only display sales data for a specific region or product.
• Page-level filters apply to all visuals on a single report page, enabling consistency across different visualizations. For example, a filter for "Year" might apply to every visualization on a report page, showing data only for a particular year.
• Report-level filters affect all pages and visualizations within a report, ensuring a consistent dataset is used throughout the entire report.
• Slicers are interactive filters that allow users to select specific criteria on the report page itself. Slicers are often used for date ranges, categories, or hierarchical data, giving users direct control over the data being displayed.

For example, if you're working with a sales dataset, you can apply a filter on "Region" to only show data for North America, or filter by "Product Category" to display only certain products. Filters help to make data analysis more precise, relevant, and interactive in Power BI.

12. What are Custom Visuals in Power BI?

A: Custom visuals in Power BI are user-created, interactive visualizations that extend the built-in charting capabilities of Power BI. These visuals are typically created using JavaScript and can be used to display unique data insights that aren’t covered by standard visuals.

Example: In a marketing team, custom visuals could be used to create interactive geo-maps displaying customer distribution across regions, or a custom funnel chart to track conversion rates through different stages of a sales pipeline, providing deeper insights than default visuals.

13. What is the purpose of GetData in Power BI?

A: Get Data in Power BI is a feature that allows users to connect to various data sources and import data into Power BI for analysis and reporting. It serves as the first step in the data preparation process, where you can bring data from multiple sources such as databases, Excel files, cloud services, APIs, and web data into Power BI.

Example: A business analyst can use Get Data to connect to a SQL Server database containing sales data and then import that data into Power BI to create interactive dashboards and reports for the sales team, enabling them to analyze performance and trends.

14. What are some benefits of using Power BI?

A: Power BI enables users to create reports and dashboards that provide actionable data for decision-making. Here are some of its benefits:

• Easy-to-use drag-and-drop interface for quick report creation.
• Connects to a wide range of data sources for comprehensive analysis.
• Real-time data updates for accurate, up-to-date insights.
• Interactive visualizations to explore data dynamically.
• Affordable pricing with sharing and collaboration capabilities for teams.

15. What are the limitations or challenges of Power BI?

A:  Power BI has several limitations, such as handling large datasets, complex customizations, and performance issues with large models. 

• Power BI has limitations on the amount of data it can handle, especially with the free version. Large datasets may require additional configurations or premium subscriptions.
• While Power BI is user-friendly, highly complex customizations or advanced analytics may require a steep learning curve or external tools.
• Although Power Query provides powerful transformation options, certain advanced data manipulation may still be limited compared to other specialized tools.
• Power BI performance can degrade when handling very large models, complex calculations, or excessive use of calculated columns and measures.
• Power BI’s cloud-based features rely on a stable internet connection, which can hinder usage in environments with limited or inconsistent connectivity.

16. How does data modeling in Power BI Desktop compare to Power Pivot in Excel?

A: Data modeling in Power BI Desktop and Power Pivot in Excel serve similar purposes but differ in capabilities and use cases. Here’s a comparison table:

Also Read: What is Data Model in DBMS? What is RDBMS?

17. What different connection modes does Power BI offer?

A:  Power BI offers several connection modes to import and work with data from various sources. These modes allow users to choose the best option based on their data size, refresh requirements, and performance considerations.

Here are the key connection modes in Power BI:

18. What are the available data refresh options in Power BI?

A: Power BI offers several data refresh options to ensure that reports and dashboards remain up to date with the latest data. The refresh options allow users to control when and how the data is updated, based on the source and connectivity mode.

Here are some of the data refresh options:

19. What types of data sources can Power BI connect with?

A:  Power BI can connect to a wide variety of data sources, making it a versatile tool for data analysis and reporting. Below is a table of common data sources Power BI can connect with, categorized by type:

20. How would you describe a Power BI dashboard?

A: A Power BI dashboard is a single, interactive canvas that provides a high-level view of your data through visualizations like charts, graphs, and maps. It aggregates data from multiple sources, offering real-time insights at a glance. 

Dashboards in Power BI allow users to monitor key metrics and make data-driven decisions efficiently.

Power BI dashboards typically feature:

• Visualizations: Charts, graphs, maps, and gauges that represent the data in an easy-to-understand format.
• Interactivity: Users can click on elements to drill down into specific data or filter results.
• Real-time Data: Dashboards can display real-time data, offering up-to-date insights.
• Data Aggregation: Multiple reports and datasets from various sources can be combined in one dashboard.

Example: A sales manager might use a Power BI dashboard to view key metrics such as sales performance, regional comparisons, and product trends. Each element on the dashboard updates dynamically as the data changes, allowing for quick, informed decision-making.

21. How are relationships between tables established in Power BI Desktop?

A: In Power BI Desktop, relationships between tables are established through a process called modeling, where you connect different tables based on common fields (columns). This allows Power BI to combine data from multiple sources into a single, unified report.

Here’s how relationships are typically set up:

• Using a Common Column: Relationships are built by identifying a common column in two or more tables, typically using keys like ProductID, CustomerID, or OrderID.

  The column in one table (the primary table) is related to the corresponding column in another table (the secondary table).

• Creating Relationships: In Power BI Desktop, go to the Model view. Drag a column from one table and drop it onto the matching column of another table.

  Power BI will automatically create the relationship if the columns share the same data type. You can manually define relationships by right-clicking in the Model view and selecting "Manage Relationships."

• Types of Relationships:

  One-to-Many (1:*): A single row in the primary table can relate to many rows in the secondary table. This is the most common relationship type.

  Many-to-Many (M:M): Multiple rows in one table can relate to multiple rows in another table.

  One-to-One (1:1): Each row in both tables corresponds to exactly one row in the other table.

• Setting Cardinality and Cross Filter Direction: Cardinality determines the relationship type (e.g., 1:1, 1:*). Cross-filter direction controls how filters are applied between related tables (e.g., single direction or both directions).

Example: If you have a Sales table with CustomerID and a Customers table with CustomerID and CustomerName, you can create a one-to-many relationship by linking the CustomerID in Sales to the CustomerID in Customers. This relationship allows you to view the customer name alongside the sales data in your reports.

22. Can multiple functional relationships exist between two tables in Power Pivot?

A: Yes, multiple functional relationships can exist between two tables in Power Pivot, but only one relationship can be active at a time for filtering. You can define inactive relationships for different purposes and use DAX functions like USERELATIONSHIP() to switch between them during calculations.

Let’s consider a scenario where you have two tables:

• Orders: Contains OrderID, CustomerID, ProductID.
• Products: Contains ProductID, Price.

You want to calculate total sales using:

• Product-based relationship: Orders[ProductID] → Products[ProductID]
• Customer-based relationship: Orders[CustomerID] → Customers[CustomerID]

Example:

• Total Sales by Product (default active relationship):

Total Sales by Product = SUMX(Orders, Orders[Quantity] * 
RELATED(Products[Price]))

• Total Sales by Customer (using USERELATIONSHIP()):

Total Sales by Customer = 
CALCULATE(
    SUMX(Orders, Orders[Quantity] * RELATED(Products[Price])),
    USERELATIONSHIP(Orders[CustomerID], Customers[CustomerID])
)

Explanation:

• The first formula calculates sales by product.
• The second formula switches to the customer relationship for sales by customer using USERELATIONSHIP().

Conclusion: You can switch between multiple relationships in Power Pivot using USERELATIONSHIP() for different calculations.

23. Is it possible for a table to exist without connections to others in Power BI?

A: Yes, it is possible for a table to exist without connections to others in Power BI. This type of table is often referred to as a disconnected table.

Use Case: A disconnected table is useful when you need a table to provide values for calculations but don’t need to establish a relationship with other tables. 

For example, you might use it for what-if scenarios, parameter tables, or for creating custom slicers that don’t directly interact with other data.

Example: If you want to create a What-If scenario for sales growth, you can have a table with growth percentages, but it doesn't need to be linked to other data tables.

You can use this disconnected table to calculate projected sales in a measure, without connecting it directly to any other tables.

While disconnected tables are not linked by relationships, you can still leverage them in DAX calculations by using functions like LOOKUPVALUE or CROSSJOIN.

24. What is the purpose of the CALCULATE function in DAX?

A: The CALCULATE function in DAX is used to modify the context in which a calculation is performed. It allows you to adjust filters or apply specific conditions to a calculation dynamically. Essentially, it evaluates an expression in a context modified by the filters you provide, enabling more complex and flexible calculations.

Example:

Total Sales in 2020 = CALCULATE(SUM(Sales[Amount]), Sales[Year] = 2020)

In this example, CALCULATE sums the Sales[Amount], but only for rows where the Year is 2020.

25. How does Power BI assist in data-driven decision-making? (New question)

A: Power BI assists in data-driven decision-making by allowing organizations to visualize data through interactive dashboards and reports. It integrates data from various sources and presents it in a way that is easy to understand and analyze. Decision-makers can explore trends, monitor KPIs, and make strategic choices based on real-time insights.

Example: A retail company uses Power BI to track sales performance across multiple regions. By analyzing visual reports on sales trends and customer demographics, managers can make informed decisions about inventory allocation and marketing strategies, optimizing performance in high-demand areas while reducing waste in others.

Also Read: Data-Driven Success with Business Analytics for Decision-Making

26. Where is data stored within Power BI?

A: In Power BI, data is stored in two main locations:

• Power BI Service: When you publish reports to the Power BI cloud service, the data is stored in the Power BI service’s cloud database, also known as Power BI datasets. These datasets can be refreshed based on the schedule you set.
• Power BI Desktop: In Power BI Desktop, data is stored in a local Data Model. This data is kept within the .pbix file and is not live-connected unless you configure DirectQuery.

Additionally, Power BI can connect to and pull data from various sources like Excel files, databases, online services, and APIs. 

Once imported, it keeps a copy of that data in either the cloud or local data model, depending on where it’s being used.

27. What does row-level security mean, and why is it important?

A: Row-level security (RLS) in Power BI allows you to restrict data access for specific users at the row level. It means that different users can view different data in the same report based on their role or permissions. RLS is applied through filters that determine which rows of data a user can access.

RLS is important because it ensures sensitive data is protected, allowing you to control access based on the user's identity. For example, a sales manager from one region might only see data relevant to their region, while another manager from a different region sees theirs. This enhances data security and ensures compliance with privacy regulations.

28. Why should Power BI data be formatted correctly?

A: Proper data formatting in Power BI is essential for accurate analysis, smooth data processing, and meaningful visualizations. Well-formatted data ensures that Power BI can interpret and aggregate information correctly, preventing errors in calculations, visualizations, and reports. It also makes the data more consistent and easier to read for users.

For example, ensuring that dates are formatted correctly allows Power BI to identify them as time-based fields, enabling time-series analysis. Similarly, consistent numerical formatting helps in applying calculations like sums, averages, or percentages. Without proper formatting, Power BI may misinterpret the data, leading to incorrect insights and decisions.

Also Read: Power BI Architecture: Components, Function, Benefits & Applications

Now that you've covered the basics, let's move on to more complex Power BI interview questions and answers for those with some experience. 

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Intermediate Power BI Interview Questions for Experienced Candidates

Intermediate roles involve creating complex dashboards, working with large datasets, and handling dynamic reports. You’ll be expected to collaborate with business stakeholders to build interactive visualizations that deliver actionable insights. 

You should be comfortable with Power BI’s data model, creating relationships between tables, and managing calculated columns, measures, and tables using DAX. 

To prepare, practice advanced DAX functions, work on data model optimization, and learn how to implement features like bookmarks and drillthrough. Understanding Power BI service, publishing reports, and using Power BI gateways will also be beneficial for these roles.

Let’s explore some of the intermediate Power BI interview questions and answers.

29. What different viewing options are available in Power BI Desktop?

A: Power BI Desktop offers several viewing options to help users design and analyze reports effectively. These include:

• Report View: The primary view where you build and design your reports using visuals, tables, and charts.
• Data View: Displays the underlying data in table format, allowing you to explore and verify your data before visualization.
• Model View: Shows relationships between tables, allowing you to manage data models and create new relationships or calculated columns.

These views help streamline the process of creating, analyzing, and refining reports, each serving a different purpose in the workflow.

30. What versions of Power BI exist?

A: Power BI has four main offerings:

• Power BI Desktop: A free, downloadable tool for individual report creation, data modeling, and visualizations on a local machine.
• Power BI Pro: A paid version that enables sharing, collaboration, and publishing of reports and dashboards. Required for sharing and accessing shared content.
• Power BI Premium Per User (PPU): A flexible, cost-effective premium solution for individuals who need advanced features like AI integration and large data models without needing full capacity-based resources.
• Power BI Premium (Capacity-based): The most robust offering for enterprises, with dedicated cloud resources, larger data models, and capabilities like paginated reports, AI integration, and more.

Each version is tailored to different user needs, from individuals to large-scale enterprise requirements.

31. What are the fundamental components of Power BI?

A: The fundamental components of Power BI are:

• Datasets: Collections of data imported or connected from various sources that form the foundation for reporting.
• Reports: Interactive collections of visualizations based on datasets, used for detailed analysis.
• Dashboards: Single-page, consolidated views containing key visualizations from multiple reports for quick insights.
• Visualizations: Graphical representations of data (charts, graphs, maps) used in reports and dashboards.
• Tiles: Individual visual elements on a dashboard, representing a single piece of information or a visualization.

These components enable users to connect to data, analyze it, and present actionable insights effectively.

Also Read: Top 15 Types of Data Visualization: Benefits and How to Choose the Right Tool for Your Needs in 2025

32. What makes up the Power BI toolkit?

A: The Power BI toolkit consists of several tools that help users connect, analyze, and visualize data:

• Power BI Desktop: The primary tool for creating reports and data models.
• Power BI Service: A cloud-based platform for sharing, collaborating, and publishing reports and dashboards.
• Power BI Mobile: An app for viewing and interacting with Power BI reports and dashboards on mobile devices.
• Power Query: A tool for data transformation, cleaning, and shaping.
• Power Pivot: A data modeling tool for creating relationships, measures, and calculated columns.
• Power BI Gateway: Used to connect on-premises data sources to Power BI for real-time reporting.

These tools together form the full Power BI ecosystem, enabling end-to-end business intelligence workflows.

33. What is a content pack in Power BI?

A: A content pack in Power BI is a pre-built collection of dashboards, reports, and datasets that are shared by others, such as service providers or organizations. It allows users to quickly access relevant data and insights without having to build reports or models from scratch.

Content packs can be sourced from popular services like Google Analytics or Salesforce, or created internally by an organization. They provide a fast way to deploy standardized reports and visualizations, helping teams or businesses save time and effort in report creation and sharing.

34. How does bi-directional cross-filtering work?

A: Bi-directional cross-filtering in Power BI allows filters to be applied in both directions between related tables. When two tables are linked by a relationship, bi-directional cross-filtering ensures that a filter applied on one table affects the data in the related table, and vice versa.

For example, if you apply a filter on the "Product" table, the "Sales" table will also be filtered based on the relationship between the two. Similarly, any filter applied to the "Sales" table will influence the data displayed in the "Product" table. 

This feature is useful for more dynamic reports where interactions between related data are required for deeper insights. However, it should be used carefully, as it can sometimes lead to performance issues in complex models.

35. What are the three core principles of DAX?

A: The three core principles of DAX (Data Analysis Expressions) are:

• Row Context: Refers to the current row in a table when performing calculations. DAX functions operate within this context, evaluating each row one by one.
• Filter Context: The set of filters applied to the data. It determines which rows are included in the calculation, allowing DAX to return different results based on user interaction with filters or slicers.
• Calculation Context: A combination of both row context and filter context, which affects how values are aggregated and calculated in a formula. It helps DAX determine the correct output when multiple filters and calculations are involved.

These principles are fundamental for creating accurate and dynamic calculations in Power BI reports.

36. Why would you need a custom visual file, and how do you use it?

A: A custom visual file in Power BI is used when the default visuals do not meet the specific needs of your report or when you require specialized visualizations not available in the standard Power BI library. 

These visuals can be created using custom code (typically in JavaScript or TypeScript) and can include unique charts, graphs, or visual effects that enhance the reporting experience.

To use a custom visual, you need to:

• Download or import the custom visual file (usually a .pbiviz file) from the Power BI marketplace or a third-party source.
• Add it to your report by importing the file into Power BI Desktop through the "Import from file" option under the Visualizations pane.
• Drag and drop the custom visual onto your report canvas and configure it with your data.

Custom visuals allow greater flexibility and creativity, enabling tailored reporting solutions for more complex data analysis needs.

37. What are some common data sources found in the Get Data menu?

A: Some common data sources found in the Get Data menu of Power BI include:

• Excel: Import data from Excel files including tables, ranges, and pivot tables.
• SQL Server: Connect to SQL Server databases to query and retrieve data.
• Web: Extract data from web pages or REST APIs.
• CSV: Import data from CSV (comma-separated values) files.
• SharePoint: Retrieve data from SharePoint lists and libraries.
• Google Analytics: Connect to Google Analytics for website data analysis.
• Salesforce: Import CRM data from Salesforce.
• Text/JSON/XML: Import data in text files, JSON, or XML formats.
• Azure: Connect to Azure databases, data lakes, and other cloud data services.

These sources provide various ways to import data for analysis, allowing users to work with data from multiple platforms in a single Power BI report.

Also Read: How to Open JSON File? A Complete Guide to Creating and Managing JSON Files

38. How are data types classified in Power BI?

A: In Power BI, data types are classified into several categories to define how data is stored and processed. The main classifications are:

Numeric Data Types: Includes whole numbers and decimal numbers.

• Whole Number: Used for integers (e.g., 1, 200).
• Decimal Number: For numbers with decimals (e.g., 12.34, 45.67).

Text Data Types: Includes data represented as strings or text.

• Text: Used for alphanumeric characters (e.g., "Hello", "Product Name").

Date and Time Data Types: Represents date and time values.

• Date: Stores only the date (e.g., 01/01/2022).
• Time: Stores time values (e.g., 14:30:00).
• Date/Time: Stores both date and time values (e.g., 01/01/2022 14:30).

Boolean Data Types: Represents True/False values.

• Boolean: Stores only two possible values—True or False.

Binary Data Type: Used to store binary data such as images or files.

• Binary: Stores data in binary format (e.g., images, files).

These data types help Power BI optimize performance and ensure proper data visualization and calculation during analysis.

39. What are the most frequently used functions in the Query Editor?

A: The most frequently used functions in the Power BI Query Editor (also known as Power Query) are:

Text Functions: Used to manipulate text data.

• Text.Trim(): Removes leading and trailing spaces.
• Text.Upper(): Converts text to uppercase.
• Text.Lower(): Converts text to lowercase.

Number Functions: Perform operations on numeric data.

• Number.Round(): Rounds a number to a specified decimal place.
• Number.Abs(): Returns the absolute value of a number.
• Number.Add(): Adds two numbers together.

Date and Time Functions: Manage and manipulate date/time data.

• Date.AddDays(): Adds or subtracts days from a date.
• Date.From(): Converts a value to a date.
• DateTime.LocalNow(): Returns the current local date and time.

Transformation Functions: Used to change data structure and types.

• Table.SelectColumns(): Selects specific columns from a table.
• Table.RemoveRows(): Removes rows from a table based on a condition.
• Table.Pivot(): Converts unique column values into multiple columns.

Conditional Functions: Allows conditional logic within transformations.

• if ... then ... else: Creates conditional logic for column transformations.

These functions help clean, transform, and model data before loading it into Power BI for analysis.

40. What is meant by grouping in Power BI?

A: In Power BI, grouping refers to the process of organizing data into categories based on one or more columns. This allows users to aggregate or summarize data, making it easier to analyze and visualize.

For example, you might group sales data by product categories or regions to calculate totals or averages for each group. Grouping in Power BI can be done in several ways:

• Manual Grouping: You can manually create groups in Power BI by selecting specific data values and grouping them into categories.
• Automatic Grouping: Power BI automatically groups data when using functions like SUM(), AVERAGE(), COUNT(), etc., in visuals like bar charts or tables.
• Group By in Query Editor: In the Query Editor, you can group data to perform transformations and aggregation like sum, average, or count on selected columns.

Grouping helps in breaking down complex data, making it easier to derive insights and visualize trends.

41. What are responsive slicers, and how do they improve reporting?

A: Responsive slicers in Power BI are interactive filtering elements that adjust the data displayed on a report based on user selections. They allow users to filter data across visuals on a report, enhancing the interactivity and user experience. 

When a slicer is set to be responsive, it automatically updates the related visuals to reflect the selected filter, making reports more dynamic.

How they improve reporting:

• Users can filter the data displayed across different visuals by selecting values from slicers, giving them more control over the information they see.
• As users change their slicer selections, all relevant visuals on the report refresh immediately, providing up-to-date insights.
• With responsive slicers, users can interactively explore the data, drilling down into different segments without needing to modify the report layout.

Example: If a report shows sales data by region and product, adding a responsive slicer for "Region" will allow users to select a region. All charts and tables on the report will adjust to show data only for the selected region, improving the report’s interactivity and responsiveness.

42. What is query folding, and why does it matter?

A: Query folding in Power BI refers to the process where data transformations, like filters or aggregations, are pushed back to the data source instead of being handled in Power BI itself. 

Essentially, Power BI asks the source system (like SQL Server, Oracle, or other databases) to perform these operations before the data is imported, reducing the amount of data loaded into Power BI.

Why it matters:

• By offloading computations to the data source, query folding reduces the volume of data transferred, leading to faster data refresh and reports.
• Query folding ensures that the heavy lifting is done by the database engine, which is optimized for such operations, rather than consuming local resources in Power BI.
• With query folding, you can handle larger datasets more efficiently because the processing happens on the server, not in Power BI's memory.

Example: If you're working with a large dataset, applying a filter to exclude certain records in Power BI’s Query Editor can be folded back to the source system, so only the relevant data is pulled into Power BI.

43. What is M language, and how is it used in Power BI?

A: M language is a data query language used in Power BI's Power Query Editor to transform and manipulate data before it's loaded into Power BI. It’s a functional language that helps create custom queries and data transformations like filtering, merging tables, or applying custom calculations.

How it's used in Power BI:

• M language is automatically generated when you perform transformations in Power Query. These transformations could involve filtering rows, changing column data types, grouping data, or merging multiple datasets.
• Users can write custom M code for more complex data transformations that go beyond the default actions available in Power Query’s UI.
• M is essential for ETL (Extract, Transform, Load) processes, as it allows for cleaning, reshaping, and preparing data for analysis before loading it into Power BI.

Example: If you want to filter out rows where sales are less than a certain amount, Power Query generates the M code automatically when you apply a filter. The M code might look like:

Table.SelectRows(Sales, each [SalesAmount] > 500)

This code tells Power BI to load only rows where the sales amount is greater than 500, improving performance and data relevance.

44. What is the difference between calculated columns and measures?

A: Calculated Columns and Measures are both used to perform calculations in Power BI, but they differ in how and when the calculations are performed.

Here's a comparison table:

In short, calculated columns are used for creating new, static columns, while measures are for dynamic, context-sensitive calculations.

45. How do visual-level, page-level, and report-level filters vary?

A:  Here's a comparison table to explain the differences between visual-level, page-level, and report-level filters in Power BI:

46. What are the most common techniques for transforming data?

A: Transformation techniques help to clean, organize, and prepare data for analysis in Power BI. Here's a table summarizing the most common techniques:

47. How does the Schedule Refresh feature function?

A: The Schedule Refresh feature in Power BI automatically updates your data at specified intervals, ensuring reports reflect the most current information without manual effort. It’s essential for keeping dashboards and reports up to date with minimal user interaction.

For example, a marketing team uses the Schedule Refresh to update their campaign performance dashboard every morning. This ensures the dashboard always displays the latest statistics, so decisions can be made based on real-time data without requiring manual updates.

Also Read: Top Best Power BI Certifications to Boost Your Career in 2025

For professionals who are looking to take their Power BI skills to the next level, you must focus on advanced techniques and in-depth understanding, perfect for seasoned experts in the field.

Expert-Level Interview Questions on Power BI for Professionals

Expert-level Power BI roles demand in-depth knowledge of advanced features like Power BI Service, row-level security (RLS), performance optimization, and custom visuals. You will be responsible for leading end-to-end Power BI implementations, designing large-scale dashboards, and managing deployment processes across an organization. 

You’ll also guide teams on best practices for optimizing Power BI performance and ensuring data governance. To prepare, focus on mastering advanced DAX functions, understanding how to optimize large datasets, and integrating Power BI with external data sources. 

Familiarity with Power BI Embedded, advanced security practices, and automation techniques like Power Automate will set you apart in interviews.

Let’s explore some of the advanced Power BI interview questions and answers.

48. What information is required to create a map in Power Map?

A: To create a map in Power Map, you need data that includes geographic fields such as Country, City, Postal Code, Latitude/Longitude, or State/Province. These fields allow Power BI to plot locations on the map.

Additionally, you should have:

• Clear location-based data points (e.g., cities, countries, coordinates).
• Values that will be visualized, such as sales, population, or other metrics.
• If you want to animate data over time, including a Date or Time field is helpful.

Example: A company’s sales data includes city names and sales figures. By plotting the city names on a map, Power BI can visualize regional sales performance and identify areas for growth.

49. Which in-memory analytics engine powers Power Pivot?

A: The in-memory analytics engine that powers Power Pivot in Power BI is called VertiPaq. VertiPaq uses columnar storage and compression techniques, enabling fast querying and data processing, even with large datasets. It allows for high-performance analytics and efficient handling of complex calculations, making Power Pivot a powerful tool for business intelligence.

Example: When analyzing millions of rows of sales data, VertiPaq ensures fast response times and efficient memory usage, allowing users to perform complex queries and calculations seamlessly.

50. What are the key elements of SSAS?

A: The key elements of SQL Server Analysis Services (SSAS) are:

• Data Models: SSAS uses multidimensional or tabular models to structure data for analysis. The multidimensional model stores data in OLAP cubes, while the tabular model uses tables and relationships.
• Cubes: In the multidimensional model, cubes store data in dimensions and measures. They provide fast querying and aggregate large datasets into useful insights.
• Dimensions: These define the categories or perspectives of your data (e.g., time, geography, product categories). Dimensions help organize data for efficient analysis.
• Measures: These are numerical values or calculations (e.g., sales, revenue) that you analyze across different dimensions.
• Data Sources: SSAS connects to multiple data sources like relational databases, data warehouses, or external data sources to retrieve and analyze the data.

Example: In a sales reporting system, SSAS can model sales data using cubes, define dimensions such as region or time, and calculate measures like total sales or profit.

51. What are the three essential aspects of DAX?

A:  The three essential aspects of DAX (Data Analysis Expressions) are:

• Calculated Columns: These are columns created using DAX formulas, added directly to tables in the data model. They are computed during the data refresh and stored in the model, providing a way to perform row-by-row calculations.
• Measures: Measures are dynamic calculations that are not stored but are computed on the fly based on the current context (such as filters applied). They are essential for aggregating data (e.g., total sales, average profit) and respond to user interactions with reports.
• Context: DAX relies heavily on two types of context. Row context refers to calculations applied to individual rows in a table. Filter context refers to the set of filters that are applied to the data, affecting how calculations are evaluated.

Example: In a sales report, a measure could calculate total sales dynamically for a specific region or time period based on the filter context. 

A calculated column might be used to compute year-to-date sales for each row, and the row context would determine how this calculation is applied to each individual record.

52. What file formats are compatible with Power BI?

A: Power BI supports various file formats for importing and exporting data. The most common and compatible formats include:

• Excel (.xlsx, .xls): Power BI can directly connect to Excel workbooks, importing data from tables, ranges, and even Excel-based Power Pivot models.
• CSV (.csv): Comma-separated values files can be easily imported into Power BI, making it a versatile option for data extraction from external sources.
• Text Files (.txt): Tab-delimited or space-delimited text files are supported for importing data into Power BI.
• JSON (.json): Power BI can read JSON files, which are commonly used for web-based data and APIs.
• XML (.xml): XML files containing structured data can be imported into Power BI, especially for data from web services or configuration files.
• SQL Server (.bak, .mdf, .ldf): Power BI can directly connect to SQL Server databases and even import backups and data files.
• Power BI Files (.pbix, .pbit): Power BI desktop files (.pbix) and template files (.pbit) can be opened, modified, and shared across environments.
• Access Databases (.accdb, .mdb): Power BI supports importing data from Microsoft Access database files.
• Web Services (.xml, .json): Power BI also supports API-based file imports, enabling data pull from web services and cloud-based data sources.

These formats allow seamless data import and integration with Power BI to create reports, dashboards, and data models.

53. What are the different phases in the Power BI workflow?

A:  The Power BI workflow typically involves the following phases:

• Data Acquisition: In this phase, you gather data from various sources like databases, files (CSV, Excel, etc.), cloud services, or web data using the "Get Data" functionality in Power BI.
• Data Transformation: Data transformation happens using Power Query Editor, where data is cleaned, transformed, and prepared for analysis. This step involves removing duplicates, filling missing values, and performing other operations like filtering, merging, or aggregating data.
• Data Modeling: In this phase, you define relationships between tables, create calculated columns and measures using DAX, and build a data model that is optimized for analysis. This step ensures your data is well-structured for reporting.
• Data Visualization: Power BI provides a wide variety of visualization tools (charts, tables, maps, etc.) to represent the data in a meaningful way. In this phase, you build reports and dashboards that visually communicate insights.
• Sharing and Collaboration: Once the report or dashboard is complete, it's published to Power BI Service for sharing with others. Users can interact with the reports and dashboards, collaborate, and share insights within the organization.
• Data Refreshing and Maintenance: In this phase, you set up scheduled data refreshes to ensure that the data remains up-to-date. It also involves monitoring the performance of reports and addressing any issues that arise. 

54. Which professionals and industries rely on Power BI the most?

A: Power BI is used across various industries by professionals to analyze data, visualize insights, and support decision-making. Key sectors relying on Power BI include:

• Business Analysts: For data analysis, reporting, and dashboards to guide strategic decisions.
• Data Analysts: To explore large datasets and create actionable insights.
• Financial Analysts: For generating financial reports, tracking KPIs, and forecasting.
• Sales & Marketing Teams: To track performance, analyze customer trends, and optimize campaigns.
• Healthcare: To manage patient data, track operations, and improve outcomes.
• Retail: For sales analysis, inventory tracking, and supply chain optimization.
• Supply Chain Managers: To monitor logistics and inventory in real-time.
• Executives: For high-level performance monitoring and informed decision-making.

Industries like finance, retail, healthcare, and manufacturing are prominent users, leveraging Power BI to transform data into actionable insights for better performance and growth.

55. What is the Advanced Editor in Power BI used for?

A:  The Advanced Editor in Power BI is used to view and edit the M code behind the data transformations applied in the Power Query Editor. It allows users to make complex changes or adjustments to the data transformation process manually. 

This is especially useful when applying custom logic or advanced transformations that are not available through the graphical interface of Power Query.

For example, if you want to concatenate two columns into a new one, you could use the Advanced Editor to manually write M code like:

= Table.AddColumn(PreviousStep, "FullName", each [FirstName] & " " & [LastName]

56. What types of gateways are available in Power BI, and why are they useful?

A:  Power BI offers two types of gateways: Personal Gateway and Enterprise Gateway. These gateways are used to facilitate secure data transfer between on-premises data sources and the Power BI cloud service, allowing for data refresh and reporting.

• Personal Gateway: This is for individual use and is ideal when a single user needs to connect their local data (like Excel files) to Power BI. It supports data refresh for personal reports and dashboards. It is easy to set up but only supports a single user.
• Enterprise Gateway: This is designed for organizations and supports multiple users. It allows secure and scheduled data refreshes for large-scale deployments, handling connections to on-premises data sources like SQL Server, Oracle, and others. It is more robust and scalable than the Personal Gateway.

Example: A business using SQL Server for sales data can use the Enterprise Gateway to keep their Power BI dashboards updated with the latest data, making the reports reliable and up-to-date.

57. What are some practical applications of Power BI?

A:  Power BI is used in various industries to transform data into actionable insights. Here are a few practical applications:

• Sales & Marketing: Power BI helps track sales performance, lead conversion, and campaign success by creating real-time dashboards for better decision-making.
• Finance: Financial teams use Power BI to analyze revenue, profit margins, expenses, and forecast budgets, enabling informed financial decisions.
• Healthcare: In healthcare, Power BI is used to monitor patient data, treatment outcomes, and hospital efficiency. It helps track trends and improve patient care.
• Supply Chain Management: Power BI helps monitor inventory, shipments, and supplier performance, ensuring smooth supply chain operations and reducing costs.
• Human Resources: HR teams use Power BI to analyze employee performance, turnover rates, and recruitment metrics, optimizing workforce management.

58. How can Power BI be leveraged to tell a meaningful data story?

A: Power BI helps in telling a meaningful data story by transforming raw data into interactive visuals that provide actionable insights. 

By connecting to multiple data sources, cleaning and transforming the data, and creating compelling charts, graphs, and dashboards, Power BI allows you to highlight key trends and patterns. Users can explore the data interactively to understand the story behind the numbers.

Example: A retail company uses Power BI to track sales performance across different regions. The interactive dashboard shows sales trends over time, correlates them with marketing spend, and allows managers to drill down into specific regions for a detailed view. 

This helps decision-makers visualize the impact of their marketing strategies and adjust future campaigns accordingly.

59. What role do KPIs play in Power BI?

A: KPIs (Key Performance Indicators) in Power BI play a crucial role in measuring and tracking the performance of business objectives. They allow users to evaluate how well a company is achieving its goals by displaying key metrics in a visual format. 

KPIs are typically displayed as a combination of a value, a target, and a trend indicator, making it easy for users to quickly assess performance and take necessary actions.

Example: A sales team uses KPIs in Power BI to track monthly sales performance. The KPI shows the actual sales value, compares it to the target sales for the month, and uses color coding (green for on target, red for under target) to quickly indicate whether the team is meeting its goals. This helps the team stay focused and adjust strategies when necessary.

60. What is a Slicer, and why is it useful?

A: A Slicer in Power BI is a visual filter that allows users to interactively select and filter data in reports. It provides a simple way to narrow down the data displayed in a report, helping users focus on specific information. Slicers can filter data based on categories like date, region, product, or any other attribute available in the dataset.

Example: A sales dashboard in Power BI uses a slicer for selecting different regions. When a user clicks on "North America," the visuals on the report update to show sales data specific to that region. Slicers make the report interactive, allowing users to explore and analyze data more effectively without affecting the rest of the report.

61. What does Power BI Designer do?

A: Power BI Designer was the original name for what is now known as Power BI Desktop. Rather than being a separate tool, it was simply an earlier version that was later rebranded. Power BI Desktop offers a user-friendly, drag-and-drop interface for building reports, designing data models, and creating interactive visualizations.

It allows users to import data, transform it with Power Query, and create relationships between tables. Since the rebranding, Power BI Desktop has added enhanced capabilities such as custom visuals, advanced analytics, and better integration with the Power BI service, making it the go-to tool for data modeling and report creation.

62. What are the different ways to transform data in Power BI?

A: In Power BI, there are several ways to transform data to make it suitable for analysis and reporting. Here are the key methods:

• Power Query Editor: This is the primary tool for transforming data in Power BI. It allows you to clean, reshape, and merge data from different sources using a variety of options like filtering rows, changing data types, grouping data, and removing duplicates.
• DAX (Data Analysis Expressions): DAX is used to create calculated columns, measures, and calculated tables within Power BI. You can use DAX functions to perform complex calculations and transformations on your data.
• Transform Data Types: You can change the data types of columns to match the correct format (e.g., changing a text field to a date or numeric type).
• Merge Queries: Combine data from multiple tables by merging them based on a common key, similar to SQL joins.

These methods help ensure that the data is clean, accurate, and ready for analysis in Power BI.

Also Read: A Comprehensive Guide to the Data Science Life Cycle: Key Phases, Challenges, and Future Insights 

Proven Tips to Excel in Power BI Interviews

Power BI interviews often test both your technical expertise and your ability to solve real-world business problems. Here’s a strategic approach to help you prepare and excel:

• Master Core Power BI Concepts: Ensure a strong understanding of Power BI’s core components like data models, DAX, Power Query, and visualization tools.
• Hands-On Practice: Build real dashboards, perform data transformations, and use advanced features to familiarize yourself with different data sources and visualizations.
• Understand Common Business Scenarios: Prepare for scenario-based questions by learning how Power BI can solve real business problems like sales forecasting, trend analysis, and KPI reporting.
• Know the Latest Features: Stay updated on new Power BI updates and tools to showcase your ability to work with the latest functionality.
• Prepare for Data Modeling Questions: Be ready to discuss normalization, relationships, and performance optimization strategies in Power BI.

These strategies will help you navigate technical and scenario-based Power BI interviews effectively.

Also Read: Future of Data Analytics in India: Trends & Career Options 2025

With the right knowledge and interview tips in hand, upGrad provides courses designed to enhance your Power BI skills. Whether you're a beginner or looking to advance your expertise, upGrad's expert-led curriculum can guide you toward success.

How upGrad Helps You Strengthen Your Power BI Skills?

upGrad offers specialized Power BI courses that cover everything from basic data modeling to advanced DAX and Power Query techniques. With over 10M+ learners trained, these courses provide hands-on experience through real-world projects, helping you build skills in data visualization, report generation, and data-driven decision-making.

Here are some relevant courses to enhance your learning journey:

• Introduction to Data Analysis using Excel
• Analyzing Patterns in Data and Storytelling
• Case Study using Tableau, Python and SQL
• Professional Certificate Program in Business Analytics & Consulting
• Professional Certificate Program in UI/UX Design & Design Thinking

You can also get personalized career counseling with upGrad to guide your career path, or visit your nearest upGrad center and start hands-on training today! 

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