Power BI Interview Questions & Answers 2026

Data transformation, done in Power Query Editor, involves cleaning, filtering, and shaping data (removing duplicates, handling nulls, data type changes) before creating visualizations, ensuring the data fed into reports is accurate and reliable.

Yes, Power BI can automatically detect data types during data load. This is controlled via the setting “Always detect column types and headers for unstructured sources,” which helps avoid manual data type conversion.

M is the scripting language Power Query uses to perform ETL operations. Typically, Power BI auto generates the M code when you apply transformations via UI, but knowing M syntax helps in debugging an creating advanced custom queries.

Applied Steps record every transformation action sequentially. They are reusable and can be edited or deleted, allowing developers to track, manage, and alter data preparation actions systematically. .

You filter out nulls or unwanted rows during the transformation phase by applying filters in Power Query Editor or using DAX functions post-load to clean data before visualization.

When raw data lacks defined headers, Power BI can promote the first row of data to be column headers, which is critical to properly recognizing the dataset’s structure.

Open Power Query Editor.Select the columns you want to check for duplicates.Go to the Home tab → Remove Duplicates.Duplicates will be removed based on selected columns.

In Power Query, select the column. Go to Home → Split Column → By Delimiter. Choose the delimiter (e.g., comma, space, hyphen). Decide to split into two or more columns based on delimiter occurrence.

In Power Query, use Merge Queries. Select columns that can logically link the two datasets (even if they are not exact keys). Use a custom join (inner, left outer, etc.). After merging, expand the columns you need from the second table.

Cross-check imported data with source records. Use profiling tools in Power Query (Column Distribution, Quality, Profile). Apply summary statistics (sum, count) to verify completeness. Compare totals and counts after transformations. Perform spot checks using sampling and visual validation.

Create a separate numeric column like MonthNumber (e.g., 1 for Jan, 2 for Feb). Sort the Month-Year column by the MonthNumber column: Select column → Sort by Column → choose MonthNumber.

Yes, Power BI supports many data sources including Oracle, SAP HANA, Amazon services, IBM DB2, SharePoint, and many others, accessible via certified connectors or custom connectors.

Connecting via SQL Query means the SQL team or DB admin provides a query that pre aggregates or prepares the exact data needed, reducing developer workload. Connecting via Tables requires the developer to build relationships and transformations themselves.

Global settings affect all PowerBI reports on the desktop app, while Current File settings apply only to the specific Power BI project you are working on. This distinction helps manage configurations per user needs or per project.

If reports are slow or data does not refresh properly, clearing cache from Power BI desktop’s options can help by deleting stored temporary data, similar to clearing browser cache. Learn How to Get Started with Microsoft Power Platform! Register Now

Data refresh schedules are setup in Power BI Service, allowing datasets imported into Power BI to refresh at specific intervals (e.g., every 30 minutes), ensuring reports contain updated data.

Power BI requires authenticat on for protected data sources. It can use browser- based authentication, Microsoft credentials, or configured certificates to validate connections securely.

Preview features are experimental options released for early testing. They can be enabled in settings but should be used cautiously in production because they might change or be removed later.

Used to securely connect on-premises data sources with the Power BI Service for refresh and live queries.

Manual Refresh: Triggered by user manually. Good for ad-hoc updates. Immediate. Scheduled Refresh: Automatic at defined intervals. Good for regular updates (daily/hourly). Background and automated.

Data refresh is triggered manually or scheduled automatically. You can schedule refresh up to 8 times/day (Pro) or 48 times/day (Premium). Managed through Datasets settings in Power BI Service. Requires Gateways if the data source is on-premises.

Q&A allows users to interact with the report using natural language queries, so users can ask questions like “Which region has the highest sales?” and get instant visual answers, enhancing report interactivity.

Use line charts for churn trend over time. Cards for churn KPIs (e.g., churn rate, retention rate). Bar charts for churn reasons. Slicers to filter by region, subscription type, etc. Add drill-through pages for detailed customer analysis.

Create a new page. Add fields into the Drill-through Filters pane. Right-click visuals to drill-through to that page with filtered data.

Add a Q&A visual.ask natural language questions (like “Top n customers”) and get instant answers. Define synonyms in the model for better matching.

Use Conditional Formatting on visuals like tables or bar charts. Create a DAX measure to define thresholds: Performance Status = IF([Sales] < 10000, "Low", "Good") Apply color rules: Red for low-performing, Green for good-performing products. Optionally, add dynamic KPIs or custom tooltips.

Sales Revenue Growth Rate Customer Retention Rate Churn Rate Average Deal Size Inventory Turnover Rate Profit Margin On-time Delivery Rate

Field Parameters allow users to dynamically switch columns, measures, or axes via slicers without changing the underlying report.Created using Modeling → New Parameter → Fieds. Example: Select Sales Amount, Profit Margin dynamically on axis or chart.

Responsive slicers automatically adjust their size and layout (horizontal/vertical or list/dropdown) based on screen size and available space.This improves user experience on different devices (desktop, tablet, mobile).

Insert a Funnel visual from the Visualizations pane. Drag a Category field (e.g., Sales Stage) into Group. Drag a Measure (e.g., Total Revenue) into Values. It displays stages from largest to smallest (ideal for sales pipelines, process flows).

Hierarchical slicers allow users to filter data by drilling down into multiple related fields (e.g., Country → State → City). Instead of multiple slicers, a single slicer shows all hierarchy levels.

Co-pilot is an AI assistant feature that helps users with guidance, suggesting steps to build reports or write queries, but it does not build complete reports automatically. It’s a support tool rather than a full developer replacement.

10 GB per user in Power BI Prolicense. (subject to verified from official site on for latest update)

Define roles in Power BI Desktop. Apply DAX filters (e.g., 'Department' = USERPRINCIPALNAME()). Publish and assign users to roles.

Publish reports to Power BI Service. Share reports directly or via workspaces. Use Apps to bundle and distribute multiple reports. Set Row-Level Security for personalized views. Collaborate via Teams, SharePoint, or email links. Microsoft Power Automate Interview Question & Answer. Learn More

Connect to sources. Create relationships, DAX. Clean, filter, and enrich data. Design and build reports. Publish to Power BI Service. Share via workspaces, apps. Schedule refreshes, audit usage.

YoY Revenue Growth (%) = DIVIDE( [Total Revenue] - CALCULATE([Total Revenue], SAMEPERIODLASTYEAR('Date'[Date])), CALCULATE([Total Revenue], SAMEPERIODLASTYEAR('Date'[Date])) )

CALCULATE returns a single value by changing the filter context. CALCULATETABLE returns a table after applying new filters.

GROUPBY SUMMARIZECOLUMNS ADDCOLUMNS SELECTCOLUMNS Get a Free Career Counselling Session & Live Demo! Register Now

SUMMARIZE: Requires a table argument and is flexible but older. SUMMARIZECOLUMNS: More optimized, does not require a table argument, ignores row context.

Without FILTER: Simple column filters. With FILTER: Row-level filtering, better for complex conditions.

ALL - Ignores all filters completely. ALLSELECTED - Ignores filters but respects slicer selections.

SUMMARIZE: Requires a table argument first. Supports row context. More flexible but less optimized. Older function. SUMMARIZECOLUMNS: Does not require a table argument. Ignores row context. Optimized for performance. Newer, recommended function.

EARLIER: Refers to the previous row context. Mainly used for nested row contexts. EARLIEST: Refers to the outermost row context.Useful when multiple nested contexts exist and you want the outermost value.

Measure: A calculation returning a single value (SUM, COUNT, AVG). Logical/analytical component. KPI: Visual performance indicator for business goals. Visualizes a measure vs a target with trend indicators (good/bad).

Running Total Sales = CALCULATE( SUM('Sales'[Amount]), FILTER( ALLSELECTED('Date'[Date]), 'Date'[Date] <= MAX('Date'[Date]) ) )

Rolling 12M Avg Sales = AVERAGEX( DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -12, MONTH), [Total Sales] )

Last 7 Days Sales = CALCULATE( SUM('Sales'[Amount]), DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -7, DAY) )

Customer Category = SWITCH( TRUE(), [Total Purchase] > 50000, "High", [Total Purchase] > 20000, "Medium", "Low" )

SKIP RANK: Skips a rank after a tie (1,1,3). Next rank is +2 if tie occurs. DENSE RANK: No gaps after tie (1,1,2). Next rank is +1 if tie occurs.

Customer Rank = RANKX( ALL('Customer'[CustomerName]), [Total Sales], , DESC, DENSE )

MTD (Month-To-Date)TOTALMTD(SUM('Sales'[Amount]), 'Date'[Date]) QTD (Quarter-To-Date)TOTALQTD(SUM('Sales'[Amount]), 'Date'[Date]) YTD (Year-To-Date)TOTALYTD(SUM('Sales'[Amount]), 'Date'[Date]) These use built-in Time Intelligence functions.

Always use a proper Date Table (continuous dates, no gaps). Mark the Date Table as "Mark as Date Table" in Power BI. Ensure relationships between Date Table and Fact Table exist.

Row context vs filter context difference. Measures like AVERAGEX, RANKX, or custom aggregations cause totals to be re-evaluated, not simple sum of values. Conditional calculations inside measures can behave differently at total level. Use corrected DAX like SUMX() for consistent results.

Reduce number of visuals. Use Import mode when possible. Avoid complex DAX measures on visuals. Use aggregations and query folding.

Aggregate tables. Use DirectQuery or Composite models. Partition large fact tables. Limit number of columns and reduce cardinality.

Use Import mode instead of DirectQuery when possible. Reduce visuals per page. Avoid unnecessary columns and rows. Pre-aggregate large tables before importing. Use query folding in Power Query. Optimize DAX by using simple measures. Turn off auto date/time in the data model if not needed.

Data Modeling- Use Star Schema, remove unnecessary columns, normalize date tables, set correct data types. Data Visualization- Focus on simplicity, consistent colors, limit visuals per page, highlight insights, use tooltips/bookmarks. Data Transformation Push transformations to source if possible, maintain query folding, remove unnecessary steps, document transformations.

Identified heavy DAX calculations and optimized them (eg., reduced nested iterations). Reduced number of visuals and used summarized tables. Split large datasets into smaller partitions or pre-aggregated at source level. Enabled Incremental Refresh for large tables. Optimized relationships (e.g., single-directional filters unless bi-directional was necessary).

Use bridge tables when possible. Set relationship type to many-to-many carefully. Avoid circular relationships.

Only one active relationship between two tables. Multiple inactive relationships are allowed.

Go to Power BI Service → Workspace → Create → Dataflow. Choose Add New Tables. Connect to multiple data sources (SQL Server, Excel, Salesforce, etc.). Use Power Query Online to clean and transform the data. Save and load the dataflow. Link dataflow to Power BI Desktop to use consolidated data across multiple reports.

Append- You can append two or more tables - no fixed limit officially. You can append as many tables as your system memory and Power Query performance can handle efficiently. Merge- You can merge two tables at a time in a single merge operation. However, you can perform multiple sequential merges to combine additional tables if needed. Join Our Updated Microsoft Power Automate Course - Stay Ahead in Agentic Automation! Learn More

One-to-Many (1: relationship)* is preferable: Date table (one unique record per day) → Transaction table (many records for each date).

It's the Microsoft Power BI Data Analyst Associate certification, focused entirely on Power BI skills.

It validates expertise in data preparation, modeling, visualization, and analysis using Power BI.

PL-300 goes in-depth into the technical features of Power BI.

No formal prerequisites, but basic knowledge of data analysis and visualization helps.

On Demand Questions: (Didn’t find your question in our Power BI interview series or heard something new in a recent interview? Ask your questions. Our expert community will help you with clear, reliable answers straight from industry experience.)

It depends on your goals. If you want to become a Power BI expert, start with PL- 300 for deep, hands-on skills, and take PL-900 later for broader context. If you're new to the Power Platform, begin with PL-900 to explore all tools and decide your focus. If you're already confident in Power BI, go directly for PL-300. For business or functional roles, PL-900 alone may be sufficient.

Both PL-900 and PL-300 have no mandatory prerequisites, but choosing the right path depends on your goals. If you are new to the Power Platform, start with PL-900 to get a foundational understanding of Power BI, Power Apps, Power Automate, and other components. If your focus is on Power BI, PL-300 is the right choice, as it provides in-depth knowledge of data preparation, visualization, and analysis. If you already have a basic understanding of Power BI, you can go directly for PL-300 without taking PL-900 first. Bottom line: PL-900 is great for beginners, while PL-300 is ideal for those specializing in Power BI. Choose based on your learning path and career goals.

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