400 Power BI Interview Questions with Answers 2026

Power BI Interview Practice Questions and Answers is the ultimate resource I’ve designed for data professionals who want to move beyond surface-level tutorials and truly master the mechanics of enterprise business intelligence. I’ve built this question bank to bridge the gap between "knowing the buttons" and "understanding the logic," ensuring you can confidently navigate high-stakes interviews or certification exams by deeply exploring everything from Star Schema optimization to complex DAX filter context. Whether you are struggling with the nuances of Row-Level Security, the performance implications of bidirectional relationships, or the intricacies of Power Query M transformations, I provide the granular, "why-behind-the-what" explanations you need to think like a Senior Power BI Developer.

By focusing on real-world scenarios rather than rote memorization, I help you develop the architectural mindset required to build scalable, high-performance reporting solutions that stakeholders actually trust. Exam Domains & Sample TopicsPower BI Fundamentals & Data Modeling: Star vs. Snowflake, Cardinality, and Normalization.

DAX Mastery: CALCULATE, Time Intelligence, Iterators (SUMX), and Context Transition. Power Query & ETL: M Language, Incremental Refresh, and API Integrations. Visualization & UX: Bookmarks, Drill-through, and Dashboard Storytelling.

Power BI Service & Security: RLS, Deployment Pipelines, Gateways, and Governance. Sample Practice QuestionsQuestion 1: You are designing a data model for a large retail chain. You have a fact table with 100 million rows and a high-cardinality dimension table.

Which relationship setting is best for performance and model stability? A) Many-to-Many relationship with Bidirectional filtering. B) One-to-Many relationship with Single Cross-filter direction.

C) One-to-One relationship with Bidirectional filtering. D) Many-to-One relationship with Bidirectional filtering. E) One-to-Many relationship with "Assume Referential Integrity" disabled.

F) Many-to-Many relationship using a Bridge table with Single filtering. Correct Answer: BOverall Explanation: In Power BI, a One-to-Many relationship with Single filtering is the "Gold Standard" for performance. It follows the Star Schema principle, preventing ambiguity and reducing the memory overhead required by the VertiPaq engine to calculate filter propagation.

Detailed Option Analysis:A) Incorrect: Many-to-Many with Bidirectional filtering causes performance degradation and "ambiguous" paths in large models. B) Correct: This is the most efficient way to filter a fact table by a dimension, ensuring high performance and predictable DAX results. C) Incorrect: One-to-One relationships are rare and usually indicate that the two tables should be merged into one.

D) Incorrect: Bidirectional filtering on a Many-to-One relationship creates unnecessary overhead and can lead to circular dependencies. E) Incorrect: Disabling "Referential Integrity" usually hurts performance as the engine must perform outer joins instead of inner joins. F) Incorrect: While a Bridge table is a valid workaround for Many-to-Many, it is less efficient than a direct One-to-Many relationship if the data can be modeled that way.

Question 2: Which DAX function would you use to modify the filter context of a calculation to ignore all filters applied to a specific column while keeping filters on other columns? A) ALL()B) ALLEXCEPT()C) ALLSELECTED()D) REMOVEFILTERS()E) KEEPFILTERS()F) VALUES()Correct Answer: DOverall Explanation: While several functions deal with filters, REMOVEFILTERS is the modern, explicit DAX function specifically designed to clear filters from a designated table or column within a CALCULATE statement. Detailed Option Analysis:A) Incorrect: ALL() works, but it is also used to return a table of unique values.

REMOVEFILTERS is the preferred semantic choice for "filter removal" inside CALCULATE. B) Incorrect: ALLEXCEPT() removes all filters except for the specified columns, which is the opposite of the requirement. C) Incorrect: ALLSELECTED() ignores filters inside the query but respects "external" filters like slicers; it doesn't target a specific column exclusively.

D) Correct: REMOVEFILTERS() is specifically optimized to clear filters on a targeted column without affecting the rest of the context. E) Incorrect: KEEPFILTERS() adds to the existing filter context rather than removing it. F) Incorrect: VALUES() returns a one-column table of unique values; it does not remove filters.

Question 3: A report is running slowly in the Power BI Service. After checking the Performance Analyzer, you see the "DAX Query" time is very high. What is the most likely culprit?

A) The Gateway is not updated to the latest version. B) There are too many visuals on a single report page. C) A measure is using an iterator like SUMX over a large fact table with complex logic.

D) The data source is an Excel file stored on OneDrive. E) The "Auto Date/Time" setting is enabled in Global Options. F) Row-Level Security (RLS) is limiting the data too aggressively.

Correct Answer: COverall Explanation: High "DAX Query" time indicates that the calculation engine is struggling to process a formula. Complex iterators (like SUMX or FILTER) force row-by-row evaluation, which can be extremely slow on large datasets. Detailed Option Analysis:A) Incorrect: Gateway issues usually cause "Data Refresh" failures or "Data Source" latency, not slow DAX execution.

B) Incorrect: Too many visuals usually results in high "Other" or "Visual Display" time, not high DAX Query time. C) Correct: Iterators are computationally expensive; if used over millions of rows with nested logic, they are the primary cause of slow DAX queries. D) Incorrect: The source location affects refresh speed, not the runtime DAX performance of a published report.

E) Incorrect: Auto Date/Time increases model size (metadata), but wouldn't typically be the primary cause of a specific high DAX Query time vs. model bloat. F) Incorrect: RLS actually reduces the number of rows processed, which often makes queries faster, not slower.

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