Top Power BI Interview Questions And Answers for 2025

Power BI has become an in-demand tool for analytics and visualization in a world increasingly reliant on data. It enables users to convert disconnected data into effective knowledge with the help of interactive boards and comprehensive reports. As companies lean more into data to refine their strategy-making, the need for qualified Power BI specialists is on the rise. 

This blog is your ultimate guide to Power BI job interviews. It contains key topics, frequently asked questions, and tips to impress as you become familiar with the Power BI concepts and discover thousands of opportunities, regardless of your goals. 

Power BI has competitive salary packages for specialist roles between ₹5 LPA and ₹12 LPA in India and $80,000 to $130,000 annually in the US. It is an excellent area to become proficient in if you are looking to begin a career.

These are the top Power BI interview questions and answers that the interviewer is likely to ask. I have divided the interview questions into the following categories for your convenience:

Basic Power BI Interview Questions

1. What is Power BI?

Power BI is a Business Analytics tool developed by Microsoft. It allows users to create insights and share them, visualize their data or view it on a dashboard, and make defensible decisions via an analysis. You can connect to data from different data sources, transform the data with Power Query, and create interactive reports/dashboards using BI Desktop, Service, and Mobile applications.

2. What are the main components of Power BI?

• Power Query: Power Query is one of the most important components of Power BI for transforming data. It helps extract data from different sources, such as Oracle, SQL, Text/CSV files, Excel, etc., and even delete data from other sources.
• Power View: The Power View provides an intuitive display of the data and retrieves the metadata for data analysis. The views are interactive, and slicers and filters can be used to slice and divide the data.
• Power BI Desktop: Power BI Desktop is an integration for Power Query, Power View, and Power Pivot. It creates advanced queries, data models, reports, and dashboards, and assists users in developing their data analysis skills.
• Power BI mobile: This application runs on Android, iOS, and the Windows Operating System (OS). It provides a highly interactive display of dashboards and can also be shared.
• Power Map: Power Map provides a 3-D geospatial visualization of data. Data can be highlighted based on its geographical location, such as continent, state, city, or street address.
• Power Q&A: Power Q&A provides answers based on questions a person may ask. It is strongly integrated with Power View and can also provide graphical answers.

3. Mention some advantages of Power BI.

• A few advantages of using BI are that it can handle a huge quantity of data.
• Information can be visualized using powerful templates and visualizations.
• Users get cutting-edge intelligence technologies and powerful algorithms that are updated regularly.
• Users can have personalized dashboards that are easy to access and understand.
• Users can perform queries on reports using the DAX language.

4. What are the key differences between Power BI and Tableau?

Ease of Use

• Power BI: More of a learning curve but less intimidating, especially for Excel users. Features a drag-and-drop process that is easy for beginners.
• Tableau: Has more of a learning curve to get over, but greater flexibility and control over complicated visualization.

Pricing

• Power BI: Offers more affordability.BI Desktop is free, while Power BI Pro is only about $10 per user per month.
• Tableau: Has a higher price point. Tableau Creator is about $70 per user per month.

 Integration

• Power BI: Integrates well with Microsoft products (Excel, Azure, Office 365, Teams, and SharePoint)
• Tableau: Has some robust integrations but no deep native support for Microsoft services.

Data Visualization

• Power BI: Provides a variety of visuals, as well as custom visuals through the marketplace. Visuals provide decent interaction capabilities.
• Tableau: More effective for advanced, customized, and polished visualizations.

Performance

• Power BI: It tends to perform better when importing medium to large datasets and has good performance when using and optimizing the VertiPaq in-memory engine.
• Tableau: During testing, it tends to perform better with very large datasets, but it does depend on some optimization and configurations.

Deployment Options

• Power BI: Cloud-first with Power BI Service; also on-premises via Power BI Report Server.
• Tableau: Flexible deployment options: on-premise, public cloud, or fully hosted on Tableau Online.

Community and Support

• Power BI: A Larger Microsoft community with regular updates. Monthly updates.
• Tableau: Well-established and strong user community with a long history of innovative data visualization protocols.

5. What exactly is Power Query?

Power Query is a data connection and transformation tool that allows users to import data, clean and reshape it, and blend data from everything before loading it into the data model.

6. What is DAX?

Data Analysis Expressions is a formula language in Power BI, Power Pivot, and SSAS that enables users to define custom calculations and expressions using calculated columns, measures, and tables.

7. What are the views on Power BI Desktop?

• Report view: Build and form reports using various visuals.
• Table view:BI displays data in rows and columns, allowing sorting, Filtering, Conditional Formatting, & Detailed Analysis.
• Data view: View, explore, and inspect the underlying data tables.
• Model view: Establish and manage relationships, create measures, and prescribe data model architectures.

8. How does a calculated column differ from a measure?

• Calculated Column: It is stored in the table and computed row-by-row at the time of data load.
• Measure: It is calculated on-demand, using DAX when the visual is rendered. This is often much more efficient if you want aggregate information.

9. What is a dashboard in Power BI?

A dashboard is a single-page interactive canvas that pinballs visuals from different reports. A dashboard is used to see an overview of key metrics and KPIs and is available only in the  BI Service.

10. What are the different filters found in Power BI?

• Visual-level filters: Filters that are applied to a single visual
• Page-level filters: Filters that are applied to all visuals on a single page.
• Report-level filters: Filters that are applied to all pages in a report.
• Slicers: a visual filter that allows users to make selections.
• Drillthrough filters: Pass context to another report page.
• Cross filters/highlights: Just that interaction between visuals.

11. What are the different connectivity modes in Power BI?

• Import: Data is imported and stored in memory.
• DirectQuery: Data is queried directly from the first source.
• Live Connection: Used with SSAS; No data stored in BI.
• Composite Models: Blending Import and DirectQuery in one model.

12. Can Excel files be linked to Power BI?

It is possible to directly connect Excel files (.xlsx,.xls) to Power BI. Tables, named ranges, and data models, for instance, can all be imported from Excel.

13. Can Power BI handle data in real-time?

It can make real-time dashboards with Azure Stream Analytics, streaming datasets, or RESTful API pushes.

14. What does the Power BI service imply?

The BI service is another cloud-based SaaS component of the Power BI. Anybody can use virtually any web browser to publish, share, collaborate on, and view reports and dashboards.

15. What is Power BI Desktop used for?

It is a program used to prepare reports, data models, and visuals at no cost. The primary members of the Power BI Desktop application user group consist of developers and analysts who usually perform development and testing before publishing to the BI Service.

16. What is Power BI Gateway?

The Power BI Gateway secures a connection between on-prem data sources and the Power BI Service to securely transmit the data.. It also features two modes: a. Personal Mode, for individual consumers, and b. Enterprise Mode is for centralized/shared use by teams.

17. What is Power BI Q&A?

Q&A encourages users to ask natural language questions (e.g., “What are total sales by region?”) and gives a visual response, leveraging artificial intelligence with linguistic analysis.

18. What are slicers in Power BI?

Slicers are UI visuals that allow on-report filtering. They allow users to choose values to filter the data in a report interactively.

19. What is the difference between Import Mode and DirectQuery Mode?

• Import Mode: Data is loaded into Power BI’s in-memory engine, allowing faster performance.
• DirectQuery Mode: Data resides in the source; queries are run on demand, so data access is real-time but slower.

20. What different data refresh options are available in Power BI?

• Manual Refresh: Triggered by user intervention.
• Scheduled Refresh: Set at intervals on the Power BI Service.
• Live Connection: No refresh is needed; data is current.
• API/Push Data: Data is pushed programmatically through REST APIs for real-time updates.

Next up, we’ll discuss Intermediate Power BI Interview Topics.

Intermediate Power BI Interview Questions

21. What is a star schema?

A star schema organizes data into fact tables, which represent measurable events (i.e., sales), and dimension tables, which represent attributes that can be described (i.e., dates, products). This format makes queries easier and improves performance because it reduces redundancy.

• Tables of dimensions: include descriptive attributes and unique keys (e.g., ProductKey, ProductName)
• Fact tables: Keep track of numerical metrics (like sales amount) and foreign keys that relate to dimensions.

22. What can you do to build relationships in Power BI?

Relationships between tables are fundamental in creating correct and interactive reports and dashboards in Power BI. By connecting tables through common fields, Relationships make analyzing and visualizing related data easy.

Relationship Types

• One-to-Many (1:*): Most frequently, one table (usually a dimension table) contains unique values, but the other table (usually a fact table) may contain duplicates.
• One-to-One (1:1): In both tables, every value in the associated columns is distinct.
• Many-to-Many (:): The related columns in both tables may contain duplicate values. Power BI natively supports this, but careful modeling is needed.

How to Automatically Establish Relationships (Autodetect)

• Power BI attempts to automatically identify and establish relationships by matching column names and data types when you load data.
• It establishes the relationship, including filter direction and cardinality, if it is confident.

Manual Creation

• To manually establish or modify relationships:
• Select the Modeling Tab.
• Select Manage Relationships > New.

Set Up the Connection

• Choose the first table and the column to relate to in the dialog box.
• Choose the corresponding column and the second table.
• Power BI suggests the relationship type (cardinality), cross-filter direction, and whether the relationship should be active.

Configure the filter direction and cardinality.

• Cardinality: Select between many-to-many, one-to-many, or one-to-one.
• Cross-filter direction: Both (bi-directional filtering) or Single (default).
• Active/Inactive: Two tables can only have one active relationship at a time; all other relationships are inactive and can be activated using USERELATIONSHIP in DAX calculations.
• To establish the relationship, click OK.

Drag and Drop (Model View)

To visually establish a relationship in the Model view, drag a field from one table onto the corresponding field in another table.

23. Describe Row-Level Security (RLS)

RLS limits access to data according to user roles.

• Static RLS: preset filters (Region = “East”).
• Dynamic RLS: DAX expressions such as USERPRINCIPALNAME() are used to modify filters.

24. How can the performance of Power BI reports be improved?

• Data Model: Reduce cardinality, eliminate unnecessary columns, and use a star schema.
• DAX: Use variables and substitute measures for calculated columns.
• Visuals: Use Performance Analyzer to find bottlenecks and limit the number of visuals on a page.

25. What are Calculated Tables in Power BI?

In addition to bringing in tables from external data sources directly, you can build calculated tables in Power BI using DAX (Data Analysis Expressions) formulas. This means you can use a computed table as an intermediate calculation or for data transformations, or if you want to create a new construct you need for your analysis. These tables are being made with data already in your model.

How Calculated Tables Work

• In Power BI Desktop, you define a calculated table by selecting Modeling> New Table.
• After that, you create a DAX formula that yields a table. To shape your new table, for instance, you could use functions like SUMMARIZE, FILTER, VALUES, UNION, or CROSS JOIN.
• Calculated tables function similarly to ordinary tables, allowing you to add calculated columns, use them in visuals, and establish relationships.
• Except in specific DirectQuery scenarios, they are recalculated whenever the data in the underlying tables is updated or changed.

Example:
CalCtable = CALCULATETABLE(Sales, Sales[Quantity]

26. What is a Measure in Power BI?

A measure is a DAX formula that works with your data to calculate a single scalar value, like sums, averages, or ratios. Measures are dynamic; they compute outcomes according to the slicers, filters, and row selections that are currently in use in your report.

How Measures Operate

• Modeling > New Measure in Power BI Desktop is used to create measures.
• They are calculated dynamically as users interact with the report rather than being saved as data in the model.
• KPIs (Key Performance Indicators) and aggregates are commonly measured.

For instance, to determine total sales:

Total Sales = SUM(Sales[Amount])

27. How do you manage many-to-many relationships in Power BI?

This is done using a bridging table to join two-dimensional tables – for example:

• Create a ProductCategory table.
• Join it in Products and Categories in a one-to-many relationship.

28. What is incremental data refresh?

Incremental refreshes any new/modified data (i.e., the last 30 days) instead of the entire dataset, so it will take less time and resources.

29. How do you use variables in DAX?

You can save an expression’s result to a variable in DAX and utilize it repeatedly in your formula. Your DAX code’s readability, performance, and maintainability may all be enhanced by doing this. The VAR keyword is used to define variables. You use the RETURN keyword to return their values.

Syntax and Structure
VAR VariableName = Expression
RETURN
FinalExpressionUsingVariable

You can declare variables before the RETURN and then use them in the final calculation.

30. Describe the use of the RELATED function in DAX.

DAX provides the RELATED function to access a value from a related table using an established relationship in your data model. It is frequently used in calculated columns or measures when you want to incorporate information from another table.

How It Works

• The RELATED function will only work when a relationship exists (typically a single-directional one-to-many relationship between the current table in context and the target table).
• A common use case is to “fetch” data from the “one” side of a relationship in the “many” side of the relationship (for example, bringing the product category from a Products table into a Sales table).

Syntax

RELATED(TableName[ColumnName])

31. Explain the Power BI Dataflow.

A BI Dataflow is a cloud-based tool for data preparation and transformation in the Power BI ecosystem. Dataflows enable developers and business users to load, clean, transform, and ingest data from various sources into  BI within the browser-based Power BI Service

32. How can Power BI data refreshes be scheduled?

If you schedule data refreshes, your Power BI datasets and dataflows will always use the most recent data from their sources.

How to Plan a Data Update:

Access the Power BI Service: Launch your web browser and sign in to the Power BI Service.

Navigate to Workspace: Locate the workspace containing your dataflow or dataset.

Dataflow or Dataset Configurations:

• For datasets, choose Settings after clicking the “…” next to the dataset.
• To access settings for a dataflow, navigate to the dataflow in the workspace.

Planned Update:

• Locate the Scheduled Refresh section in the settings pane.
• Set the frequency (daily, weekly, or several times a day) and enable scheduled refresh.
• Set the time zone and refresh times.

Credentials: To prevent refresh failures, make sure that all data sources have valid credentials set up.

Save: To verify your refresh schedule, click Apply or Save.

33. How does Power BI Pro differ from Power BI Premium?

Power BI Pro: This version gives you complete access to the Power BI dashboard, allows you to create reports, and allows you to share and view reports indefinitely. Additionally, each user is limited to 10GB of storage.

Power BI Premium: Larger companies with a dedicated storage space for every user use the premium edition. Premium users can host data sets with up to 50GB of storage space and 100TB of total cloud storage. The monthly cost is $4995.

34. How to Publish a Power BI Report?

When you publish a Power BI report, it will be available to other users in your organization via the Power BI Service (the cloud platform). Here’s how to do that:

• Make Your Report and Save It:

Save the. Pix file after creating your report in Power BI Desktop.

• Log in to Power BI Desktop:

To log in, use your organizational account.

• Press the “Publish” button:

Click the Publish button in Power BI Desktop after selecting the Home tab.

• Select a Workspace:

Please select the destination workspace in your Power BI Service (e.g., My Workspace or a shared workspace).

• Upload and Confirmation:

The report has been uploaded to the Power BI Service. You will receive a confirmation message with a link to open the report online.

• Share and Collaborate:

In the Power BI service, you can configure permissions further, share the report with coworkers, schedule data refreshes, and add the report to dashboards.

35. Power BI Performance Analyzer

Performance Analyzer checks and shows the time you need when you carry out an action that will refresh or update all visuals (the time is shown below every visual). It also provides the ability to view, drill down, or export the information.

36. Why Use Bookmarks in Power BI?

Power BI bookmarks preserve a report page’s current state, including its filters, slicers, visual selections, and visual visibility. They make guided navigation, personalized user experiences, and interactive storytelling possible.

37. How Should Null Values in DAX Be Handled?

In DAX, the formula language used in Power BI, “null” is represented as BLANK. DAX offers several functions and strategies to effectively detect, replace, or handle blank values.

38. Measures Vs. Calculated columns in DAX

Understanding the difference between measures and calculated columns is crucial for data modeling and reporting in Power BI.

Calculated Columns

• Definition:

Calculated columns are additional columns that you add to a table in your data model using DAX formulas. The calculation is performed for each row when the data is loaded or refreshed. The results of the calculation are stored in the model.

• Evaluation context:

They are evaluated in row context, which means that each row will be independently assessed based on the formula you gave it.

• Storage:

They are physically stored in your model, which will increase the size of records and memory.

• Update frequency:

Calculated columns are only computed once when data is loaded or refreshed and will not change dynamically while using the report.

• Use cases:
• Creating new data fields necessary for filtering, grouping, or slicing (e.g., profit per row: Profit = Sales[Revenue] – Sales[Cost])
• Creating columns for relationships or keys
• Binning/bucketing your data (High, Medium, Low, etc.)

Measures

• Definition:

Measures are calculated fields that use DAX, which are dynamic calculations that aggregate or estimate based on the current filter context of a visual or report.

• Evaluation Context:

Because measures are evaluated in a filter context, calculations depend on filters or slicers and also on the specific visual and cell in which they are used.

• Storage:

Measures are not stored as data in the model. Only the measure’s formula is stored in the model; the calculation is performed based upon context in a ‘fresh’ instance every time it is requested in reports/ visuals in Power BI. Since Measures are not stored in the model, the model size does not increase.

• Update Frequency:

Measures are recalculated every time the user interacts with the report (e.g., filter, slicer, etc.).

• Use Cases:

Aggregations (i.e., SUM, AVERAGE, COUNT, etc.)

KPIs and dynamic calculation (i.e Total Sales = SUM(Sales[Amount]))

Instances of calculation that change based on the selection or context of the report.

39. Power BI with Azure services?

Some themes surrounding working with Azure and Power BI include the following:

• Data Integration: Power BI integrates with Azure services like Azure data lake storage, Azure SQL Database, Azure Synapse Analytics, and Azure Cosmos DB to provide seamless access to a familiar user environment and analyze the data from the user’s Azure services of choice.
• Scalability: Azure provides a cloud-based infrastructure that is scalable for data storage and processing, so  BI can continue to work with large and complex datasets for analytics.
• Real-Time: Quickly and efficiently work with real-time data using Azure via seamless integration with Azure Stream Analytics and Event Hubs, and allow Power BI to pull real-time data for real-time analytics and monitoring.
• Centralized Data: Azure provides centralized governance, security, and data management while also using data from Power BI reports and dashboards.
• Advanced Analytics: The integration with Azure Machine Learning and Azure AI services provides further advanced analytics capabilities in BI for predictive Modeling and data intelligence.
• Power BI Embedded: Azure can embed BI reports and dashboards in your custom applications, allowing users to interact with the analytics directly inside your software environment.
• No-code Connectivity: Power BI has built-in connectors for Azure services, and having these easily set up makes everything easy without extensive coding.
• Hybrid Connectivity: Power BI connects to Azure services running on-premises with the help of an Azure Data Gateway, a software that connects cloud services to gateway data sources.
• More Informed Decisions: Azure’s data processing capabilities, combined with Power BI’s visualization options, allow organizations to be data-driven and create insights to help with better decision-making.

These points show how the combination of Power BI and Azure provides an analytics platform that is powerful, scalable, and flexible for organizations.

40. Write a DAX formula for a specific calculation (e.g., total sales, average sales per customer)?

• Total Sales:

Total Sales = SUM(Sales[Amount])

• Average Sales per Customer:

Avg Sales per Customer = AVERAGEX(Customer, [Total Sales])

Next, we’ll examine Power BI Interview Questions That Are Advanced and Scenario-Based.

Advanced & Scenario-Based Power BI Interview Questions

41. Explain a complex DAX calculation that you have written and its use

A frequent complex DAX calculation is Year-over-Year (YoY) Sales Growth. Essentially, this entails comparing sales from the current period to sales from the same period from the previous year while dealing with context and improving performance with variables:

Sales YoY Growth% % =

VAR SalesPriorYear =

    CALCULATE([Sales], PARALLELPERIOD('Date'[Date], -12, MONTH))

RETURN

    DIVIDE(([Sales] - SalesPriorYear), SalesPriorYear)

The measure calculates the percentage change in sales when compared to the previous year’s similar period and utilizes variables to handle repetitive calculations and run more efficiently.

42. How do you import and model data from multiple sources in Power BI and Power Pivot?

• Power BI Desktop: Use “Get Data” to connect to a data source (SQL Server, Excel, web, etc.). Use Power Query to perform any transformations or cleaning of the data. In the Model view, create relationships between the tables, calculate columns or measures, and design a star schema model.
• Power Pivot (Excel): Use “Manage Data Model” to import data from multiple sources. Clean the data using Power Query or Excel’s import tools. Define relationships and calculations in the Power Pivot window. Power Pivot works within the Excel environment and lacks advanced visualization and sharing features like Power BI.

43. What types of filters are there in Power BI and Power Pivot, and what are the effects on report behavior?

Filters can be categorized as:

• Visual‐level filters: apply to only one visual.
• Page‐level filters: apply to all visuals on a specific page of a report.
• Report‐level filters: apply to all report pages and all visuals in the report
• Manual filters: set in visuals or queries.
• Slicers are visual filter controls that allow users to change.
• Power Pivot: filters are primarily dealt with through PivotTable fields/filters and slicers.
• Impact: filters define what data is shown for analysis and how calculations are derived. They also determine which data is passed to visuals and the context of calculations and results across visuals and reports.

44. What are your methods of debugging and optimizing slow DAX queries in Power BI or Power Pivot?

• Use a performance analyzer in Power BI to find the slow visuals.
• Use DAX Studio to see the query plan and execution time.
• Simplify your DAX expression, use variables, and reduce row context transitions.
• Remove unnecessary columns or visuals.
• Optimize your data model (star schema, reduce cardinality)

45. Describe incremental refresh and how you would set it up in Power BI.

Incremental refresh processes only new or updated data instead of refreshing all data.

To set this up:

• You must have a date/time column defined in your table.
• In Power BI Desktop, go to Model view > Table > Incremental Refresh.
• You define how much data to keep by setting the parameters for the range of data in the table (e.g., a refresh that takes the last 1 month of data and puts the older data into an archive).
• Publish your report to the Power BI Service and schedule a refresh.

46. Describe a situation in which you created a Power BI report using multiple data sources (e.g., SQL Server, Excel, SharePoint).

For example, I may have sales data coming from SQL Server, budget target data from Excel, and employee data from SharePoint. I would clean the data in Power Query, do all merging in Power Query, build relationships in the model, and develop one report and storyline dashboard that blends the data from different sources.

47. What does version control and collaboration look like for Power BI reports and datasets?

• Store your PBIX files in OneDrive or SharePoint to have a version history and the ability to co-author.
• Use BI Service deployment pipelines for development, testing, and production processes.
• Use the ALM Toolkit to compare and deploy the dataset schema.
• As with any collaborative service, you should document what has changed and make use of your access control to ensure users are collaborating effectively on the project.

48. What do you do to protect data security and privacy in Power BI?

• Implement Row-Level Security (RLS) to restrict user access to specific data.
• All datasets are encrypted, and any data gateway should be secure.
• To help drive your data security practices, set your tenant settings to limit sharing, publishing, and exporting as needed.
• Use certified datasets so data users know they should be using that data source as their true, governed data.

49. What is your approach to handling large datasets (10 million+ rows) in Power BI or Power Pivot?

• Use aggregations and summary tables.
• Reduce granularity in the data and drop unnecessary columns.
• Use import mode for fast speed in memory, or use DirectQuery mode for real-time.
• Use column store indexing and data types most efficiently.
• To work with large data sources, you can partition them using an incremental refresh.

50. What are the use cases for Composite Models in Power BI, and how do they differ from the in-memory model of Power Pivot?

• Composite Models: This allows the import and DirectQuery tables to be combined into a single model, allowing for hybrid data access and more space to build the model.
• Power Pivot only supports in-memory (import) models in Excel, not DirectQuery or hybrid.

51. How do you create time intelligence calculations in DAX (i.e., YTD, MTD, comparison of the previous year)?

Time intelligence calculations performed in DAX are very useful for understanding the data over time – YTD, MTD, and QTD for current comparison and prior years- to obtain more insight into trends, seasonality, and performance over time. Time intelligence calculations are based on a data table with a tiled calendar formation and date. You should configure & mark it as a Date Table in your model to allow DAX to understand the time intelligence functions correctly.

Common DAX Time Intelligence Functions:

• TOTALYTD: Calculates the cumulative total from the start of the year to the current date

Sales YTD = TOTALYTD([Total Sales], 'Date'[Date])

• TOTALMTD: Cumulative total from the start of the month to the current date.

Sales MTD = TOTALMTD([Total Sales], 'Date'[Date])

• TOTALQTD: Cumulative total from the start of the quarter to the current date.

Sales QTD = TOTALQTD([Total Sales], 'Date'[Date])

• SAMEPERIODLASTYEAR: Returns the corresponding period in the previous year, useful for year-over-year comparisons.

Sales Last Year = CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))

• DATEADD: Shifts dates forward or backward by a specified interval (e.g., months, years).

Sales Previous Month = CALCULATE([Total Sales], DATEADD('Date'[Date], -1, MONTH))

• PARALLELPERIOD: Returns a parallel period for custom intervals.

Sales Parallel Period = CALCULATE([Total Sales], PARALLELPERIOD('Date'[Date], -1, YEAR))

52. What are the challenges of many-to-many relationships, and how do you solve them using techniques like bridge tables or DAX?

• Challenges:

A many-to-many relationship occurs when both tables in a relationship contain duplicates in the columns being related to, which can cause ambiguity and double-counting possibilities.

Sometimes, the aggregations you are expecting can be inaccurate, or filters may be behaving unpredictably.

• Solutions:

Bridge Table (Intermediary Table):

You can build a bridge (or junction) table that contains the unique pairs of linking keys. Link both original tables to the bridge table using one-to-many relationships. This will eliminate ambiguity and ensure you have good aggregations.

• DAX Techniques:

Although there are DAX techniques you can use for many-to-many, such as TREATAS, which allows you to filter on columns related to other tables that don’t have explicit relationships, or CROSSFILTER, which enables you to specify the direction that filters propagate across relationships.

• Native Many-to-Many: Now allows for direct many-to-many relationships. However, for clarity regarding the model and performance, bridge tables should be your go-to for complicated situations, regardless of the native option.

53. How do you develop and manage calculated columns and measures in Power Pivot vs Power BI?

Power BI:  

• Calculated Columns:  Calculated columns can be created using DAX in either the Data or Model view. They are used for row-level calculations and ultimately stored in the model.  
• Measures: Measures are created in the Modeling tab. They are dynamic calculations that are context-aware when used in visuals.    
• Management: Calculated columns and measures are located in the Fields pane; measures may be grouped into folders, while computed columns may not be grouped.

Power Pivot (Excel):

• Calculated Columns: These are dedicated columns added to the Power Pivot window with DAX. Calculated Columns are great for static calculations at the row level.
• Measures (Calculated Fields): These are set up in the calculation area of Power Pivot; measures are used in PivotTables and are often used as KPIs.
• KPI Setup: Power Pivot allows you to set KPIs based on measures and delivers targets visualized in Excel PivotTables.

54. Explain how you constructed a dynamic title or measure that is based on slicer selections.

To implement a dynamic title that shows the slicer selections as dynamic text (Sales for [Selected Region] in [Selected Year]):  

FOR EXAMPLE, IN DAX:

Dynamic Title =
"Sales for " & SELECTEDVALUE('Region'[Region], "All Regions") &
" in " & SELECTEDVALUE('Date'[Year], "All Years")

• You can add this measure to a Card visual or use it as the title of your report.
• Your title will adjust with the user selections, increasing the interactivity and understandability of your report.

55. What are the limitations of Power Pivot, compared to Power BI, and when would you use each?

Power Pivot Limitations:

• Visualizations: Power Pivot is limited to using Excel PivotTables, PivotCharts, or Power View, followed by visualizations (deprecated).
• Scalability: Power Pivot can handle smaller data models due to the limitations of Excel memory.
• Distribution: You can share an Excel file with any data collected in the Power Pivot model, but web-based or collaborative dashboards are not possible.
• Connections to data sources: Power Pivot has far fewer connectors than Power BI.
• Advanced features: Power Pivot has no capability for real-time dashboards, dataflows, or AI capabilities.

When to use each:

• Power Pivot:

You should use Power Pivot for Excel-centric workflows, ad hoc analysis, and small teams that already rely heavily on Excel.

• Power BI:

You should use Power BI for enterprise-scale reporting, advanced visualizations, collaboration, and cloud-based sharing.

56. You get disparate sales data in various formats from various regions. In what way would you combine and standardize this data into a single Power BI report?

In Power Query, import every source: Establish a connection to every file (Excel, CSV, databases, etc.).

• Convert Data
• For consistency, rename the columns.
• Modify the data types as necessary.

Standardize categorical values, currencies, and date formats.

Append/Merge: Create a single table with all of the regional data.

Modeling

Create connections using dimension tables (e.g., Dates, Products).

Verify the accuracy and completeness of the data.

Result

The result is a single, standardized sales dataset that is prepared for  BI analysis.

57. A stakeholder requests a department-restricted report. Explain the steps you would take to implement and test Power BI’s Row-Level Security.

Describe the roles: Select Modeling > Manage Roles in Power BI Desktop. Use a DAX filter such as [Department] = “Finance” to create a role (for example, “Finance”).

Assign Users: After publishing to Power BI Service, under dataset security settings, assign users to the proper roles.

RLS test:

• To confirm what each role can see, use Power BI Desktop’s “View as Role” feature.
• Use user accounts to test access in Power BI Service to make sure the right restrictions are in place.

58. After adding a few complex measures, the performance of your report significantly declined. How would the DAX logic be optimized and debugged?

Make use of the Performance Analyzer: Determine which images are slow.

Examine DAX:

• Store intermediate results in variables.
• Steer clear of pointless context changes and iterators.
• Refactor or simplify complicated expressions.

Enhance the Data Model:

• Eliminate any tables and columns that are not in use.
• If possible, reduce cardinality.

Outside Resources:

• To examine query plans and execution times, use DAX Studio.
• Look for bottlenecks in the queries and adjust the optimization accordingly.

59. A Power Pivot-built Excel-based dashboard needs to be converted to Power BI. What strategy would you use for this migration?

• Model for Export Data: Use Power BI Desktop to recreate tables and relationships.
• Rebuild Calculations: Use DAX to recreate all calculated columns and measures in Power BI.
• Visual Conversion: Use Power BI visuals in place of PivotTables and PivotCharts.
• Verify Outcomes: To guarantee accuracy, compare Excel and BI outputs.
• Improve: Make use of  BI tools such as interactive dashboards, slicers, and bookmarks.

60. A user queries why two related tables in your report are generating duplicate values during a client presentation. How would you diagnose and fix this problem?

• Verify the Relationships: Make sure the dimension table’s key column is unique and that the relationship is one-to-many rather than many-to-many. Examine your data to check for incorrect joins or duplicate keys.
• DAX Context: Make sure the appropriate filter context and aggregation are used in your measures.
• Model Structure: To address many-to-many problems, eliminate unclear or dormant relationships, and, if required, employ bridge tables.

Test: Verify that duplicates have been fixed by validating the results following each modification.

We will now examine the Retail Dashboard Use Case Power BI Interview Scenario.

Power BI Interview Scenario: Retail Dashboard Use Case

In one of my previous interviews, I was given a scenario to design a Power BI reporting solution that tracks sales performance, inventory levels, and customer sentiment. The scenario provided SQL Server for sales, inventory was presented in discarded Excel files, and customer sentiment was on a SharePoint list with unstructured text feedback.

I would first connect BI data sources and use Power Query to clean and standardize the Excel inventory files, correcting both structure and formatting issues. After applying the cleansing process, I could merge the different inventory files into a single model.

To focus on speeding up the performance of the BI report and maintenance volume, I would model the design for the reporting solution as a star schema with two fact tables: sales and inventory. The dimension tables would consist of stores, products, and dates. Then, I would create DAX measures for key performance indicators (KPIs): sales trends, stock list alerts, and store-wise performance.

For the unstructured customer feedback, I suggested using Azure Cognitive Services for sentiment analysis and bringing those insights back into BI for visualization.

I also stressed user experience, breeding interactivity into visualizations with slicers, adding drill-through, planned refresh, and Row-Level Security so users only had data relevant to them.

This interview scenario provided me the opportunity to present my practical skills, exhibiting my ability to work with dirty data, model data, perform advanced analytics, and secure reporting.

We will discuss interview tips and conclude later.

Interview Tips

• Recognize the basics

Understand how to use Power BI’s desktop, service, and mobile versions, as well as Power Query, DAX, data modeling, and report publishing.

• Scenario-Based Practice Questions

Prepare for real-world scenarios like handling slow dashboards, merging multiple data sources, or building executive-level dashboards.

• Show Off Finished Projects

Be ready to outline all of your specific contributions to a Power BI project, from sharing reports to connecting data.

• Understand the Fundamentals of Data Modeling

Learn how to manage relationships, handle fact/dimension tables, and apply a star schema.

• Power Query and Master DAX

Discover how to use Power Query and DAX to write calculated columns, measures, and transformations. 

• Review the Best Practices for Visualization

The Best Practices for Visualization are regularly tested by interviewers using logic viewers.

Explain why you chose particular charts, drill-throughs, tooltips, slicers, and KPI cards for impact and clarity.

• Make Deployment and Governance Clear

Recognize version control procedures, workspace management, app publishing, and Row-Level Security (RLS). 

Additionally, for move references, view the Edureka Power BI interview question and answer video.

Conclusion

Power BI is a powerful and popular tool used for data analysis and BI, and it opens up all sorts of career paths, from data analyst to BI developer and every role in between! Knowing some essential basics in data modeling, DAX, Power Query (M), and dashboard design is critical. Be prepared for interviews with scenario-specific questions about data security, performance tuning, or integration with Microsoft tools. BI is in high demand, and salaries are increasing in India, the U.S., and everywhere else! You should seize this opportunity to upskill in Power BI.

If you are interested in advancing your skills and your career prospects in Power BI, then you should explore the latest courses and Training programs. We recommend you take up the Power BI Certification Training offered by Edureka. Edureka’s Power BI certification course by PwC offers dual certification in business intelligence. The training is live instructor-led and provides hands-on experience in real-time projects. It prepares you for the official PL-300 exam and offers simulated real-world scenarios.

Do you require more information or have any questions? Please feel free to comment below, and we will get back to you as soon as we can!