1. How can Power BI be described and what are its primary components?
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Power BI is a Microsoft analytics and visualization platform that connects multiple data sources, transforms data and creates interactive dashboards for business insights. Its key components include Power BI Desktop for designing detailed reports, Power BI Service for publishing and collaboration and Power BI Mobile for accessing dashboards on the go. Together, these components offer a complete environment for analyzing and visualizing data effectively.
2. How does Power Query help in transforming and preparing data in Power BI?
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Power Query serves as the main tool for cleaning, transforming and shaping raw data before analysis. It supports connecting to various data sources and performing tasks such as filtering, merging, splitting and adjusting data types. This make sure that the data is accurate, consistent and ready for reporting and visualization.
3. What role does DAX play in Power BI and how does SUM differ from SUMX?
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DAX or Data Analysis Expressions, is a formula language used to create measures, calculated columns and tables for advanced analytics. While SUM simply totals values in a column, SUMX evaluates each row based on a specified expression before summing, making it suitable for detailed calculations that require row-level logic.
4. What is a data model in Power BI and why is it essential?
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A data model organizes tables, relationships and calculations to define how data interacts across a report. It integrates information from multiple sources into a unified framework, enhancing accuracy and performance. A well-structured model ensures visuals and calculations function correctly and deliver reliable insights.
5. How are relationships between tables defined in Power BI?
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Relationships are established by linking tables through common fields such as IDs or codes, ensuring proper data flow throughout the model. Power BI can detect these relationships automatically or they can be created manually. One-to-many relationships are most common, connecting a single record in one table to multiple records in another to maintain consistency.
6. What are calculated columns and how do they differ from measures in Power BI?
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Calculated columns are created using DAX and evaluated row by row, with results stored in the data model for repeated use. Measures, in contrast, are calculated dynamically during report interactions and are not stored, making them ideal for aggregations and summaries. This difference impacts report performance and how calculations respond to filters.
7. How do Power BI Desktop and Power BI Service differ in their capabilities?
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Power BI Desktop is a local tool used to connect data, prepare datasets and design interactive visuals. Power BI Service is cloud-based, offering features for publishing, scheduling data refreshes, managing permissions and enabling team collaboration. Desktop focuses on report creation, while Service provides sharing, monitoring and collaborative functionality.
8. How does Power BI ensure users only access the data they are permitted to see?
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Row-Level Security (RLS) restricts access so users only view data relevant to their role. Filters are defined within the data model based on regions, departments or categories and enforced when reports are published. This mechanism ensures sensitive or restricted data is protected and visible only to authorized users.
9. What is the difference between Import and DirectQuery modes and when should each be applied?
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Import mode loads data directly into Power BI for high performance and offline use, but requires scheduled refreshes. DirectQuery leaves data at the source and retrieves it live during report interactions, suitable for very large or frequently updated datasets, though it may reduce performance. The choice depends on dataset size, update frequency and performance needs.
10. How can performance be optimized for a complex Power BI dashboard?
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Performance can be improved by removing unnecessary visuals, deleting unused columns, simplifying DAX formulas and using aggregation tables. Reducing model complexity and optimizing queries ensures dashboards load faster while maintaining analytical accuracy. These steps enhance user experience and improve report efficiency.