Essential Power BI Interview Questions for Data Analysts Quiz

Test your knowledge with this easy Power BI quiz, focusing on key interview questions for data analyst roles. Learn about Power BI concepts, data modeling, DAX functions, filters, and best practices to prepare for your next data analyst interview.

1. Types of Filters in Power BI

   Which type of filter allows you to limit data for individual visuals only in Power BI?

   1. Data-level filter
   2. Visual-level filter
   3. Report-level filter
   4. Page-level filter

   Explanation: A visual-level filter applies to just one visual in a Power BI report, letting you control what data appears in that specific chart or graph. Report-level filters affect all visuals on all pages, while page-level filters apply to every visual on a single page. 'Data-level filter' is not an actual filter option in Power BI.

2. Power BI Platforms Overview

   What is the main difference between Power BI Desktop and Power BI Service?

   1. Desktop is used for sharing dashboards, Service is only for mobile
   2. Desktop is used for building reports, Service is for sharing and collaborating
   3. Both are only for building reports
   4. Service is used for data entry, Desktop for data storage

   Explanation: Power BI Desktop is mainly used to create and design reports, while Power BI Service enables users to publish, share, and collaborate on them online. Both are not just for report building, and neither is focused on pure data entry or storage. The Desktop app does not handle sharing dashboards directly—that is done through the Service.

3. Optimizing Performance for Large Datasets

   Which of the following helps improve Power BI performance when dealing with large datasets?

   1. Reducing model size and removing unnecessary columns
   2. Increasing report page size
   3. Relying solely on default visual themes
   4. Using only stacked bar charts

   Explanation: Minimizing the size of the data model by removing unused columns and tables can greatly enhance performance with large datasets. Changing the report page size or visuals like stacked bar charts does not impact back-end data processing. Visual themes are only cosmetic and do not speed up report performance.

4. Calculated Column vs. Measure

   Which statement best describes a measure in Power BI?

   1. An imported data table
   2. A custom visual component
   3. A dynamic calculation evaluated based on current context
   4. A static value stored for each row

   Explanation: A measure in Power BI is a dynamic calculation, such as total sales or average value, that updates based on filters and context. A calculated column stores a static value for each row. Data tables and visuals are not measures, and measures are not used for importing or creating visuals.

5. Dynamic Date Ranges

   How can you create a dynamic date range, such as 'Last 7 days', in Power BI?

   1. By manually filtering each date value
   2. By saving as a PowerPoint file
   3. By exporting report to PDF
   4. By using a DAX formula with the DATEADD or TODAY functions

   Explanation: Dynamic date ranges are achieved in Power BI using DAX functions like DATEADD or TODAY, allowing you to filter for periods like the last 7 or 30 days. Manually picking dates is time-consuming and not truly dynamic. Exporting to PDF or PowerPoint has nothing to do with filtering data in reports.

6. Import vs. DirectQuery

   What is a key difference between Import mode and DirectQuery mode in Power BI?

   1. Import is only for numeric data, DirectQuery is for text
   2. DirectQuery works offline, Import needs constant connection
   3. Import allows real-time updates, DirectQuery does not
   4. Import stores data in Power BI, DirectQuery queries source in real-time

   Explanation: With Import mode, the data is imported and stored in Power BI, while DirectQuery queries the data source each time the report is viewed. Import mode doesn't support real-time updates out of the box. DirectQuery actually requires a live source connection, unlike Import. Both can work with all data types.

7. Row-Level Security (RLS)

   What is the main purpose of Row-Level Security (RLS) in Power BI?

   1. To create colorful dashboards
   2. To restrict data access for specific users based on filters
   3. To speed up report refresh time
   4. To automatically correct data errors

   Explanation: Row-Level Security is used to restrict certain users to only see data they are permitted to, by applying data filters at user level. It is not related to report design or colors, does not directly optimize refresh performance, and does not correct data errors.

8. DAX: SUM vs. SUMX

   In DAX, what is the key distinction between the SUM() and SUMX() functions?

   1. SUMX is only for date fields, SUM for numbers
   2. SUMX can apply custom row-level calculations, SUM adds up column values directly
   3. SUM multiplies, SUMX divides
   4. SUMX sorts data alphabetically, SUM sorts by value

   Explanation: SUM totals the values of a column, while SUMX allows calculation over each row using an expression, then sums the results. The other options are incorrect—SUMX does not sort data or restrict by data type, and neither function automatically multiplies or divides.

9. Handling Many-to-Many Relationships

   Which model structure is commonly used to handle many-to-many relationships in Power BI?

   1. By introducing a bridge (joining) table
   2. By removing all relationships
   3. By creating a single table with duplicates
   4. By only using calculated fields

   Explanation: A bridge or joining table is typically used to correctly represent many-to-many relationships, ensuring accurate relationships and calculations. Duplicating data in a single table can cause inconsistencies, removing relationships defeats the purpose, and calculated fields alone do not resolve relationship issues.

10. Power Query vs. DAX

    What is the main role of Power Query compared to DAX in Power BI?

    1. Both are only used for importing raw data
    2. Power Query creates visuals, DAX creates data tables
    3. Power Query only deletes data, DAX only renames columns
    4. Power Query is for data transformation, DAX is for data analysis/calculation

    Explanation: Power Query is designed for connecting, shaping, and transforming data before it is loaded, while DAX is a formula language for computations after data is imported. The other statements are incorrect because Power Query and DAX do not primarily create visuals, delete all data, rename only, or just import raw data.