1. What is Power BI and what are its core components?
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Power BI is Microsoft’s business intelligence platform that enables organizations to integrate data from multiple sources and visualize it through interactive dashboards and reports. Its main components include Power BI Desktop for report creation, Power BI Service for cloud-based sharing and collaboration, and Power BI Mobile for viewing dashboards on smartphones or tablets. Together, these tools provide a complete ecosystem for data analysis, visualization, and insight sharing.
2. How does Power Query assist in data preparation in Power BI?
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Power Query serves as the data preparation and transformation tool in Power BI. It allows users to connect to various data sources and clean, reshape, and organize datasets before loading them into the model. Operations like removing duplicates, splitting columns, merging tables, and changing data types can be done visually, ensuring data is accurate, consistent, and ready for analysis.
3. What is DAX in Power BI, and how does SUMX differ from SUM?
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DAX (Data Analysis Expressions) is Power BI’s formula language used to create calculated columns, measures, and custom tables. The SUM function adds up all values in a column directly, while SUMX evaluates an expression row by row before summing the results. SUMX is ideal for situations requiring row-level calculations before aggregation.
4. What does a data model mean in Power BI and why is it important?
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A data model defines how tables, relationships, and calculations interact within a Power BI report. It organizes data from multiple sources in a structured way, improving report performance and accuracy. A well-designed data model ensures consistent metrics, reliable calculations, and efficient dashboards across all visuals.
5. How are table relationships established in Power BI?
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Relationships link key columns across different tables to enable meaningful analysis. Power BI can automatically detect relationships or allow users to set them manually. Commonly, one-to-many relationships connect a single record in a dimension table to multiple records in a fact table, ensuring consistency and proper aggregation in reports.
6. What are calculated columns, and how do they differ from measures in Power BI?
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Calculated columns are DAX-based fields that store values for each row in the dataset permanently. Measures, by contrast, compute results dynamically during report interaction without consuming storage space. This distinction affects memory usage, report responsiveness, and how calculations adapt to filters or slicers.
7. What is the difference between Power BI Desktop and Power BI Service?
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Power BI Desktop is a local application for connecting to data sources, transforming data, and designing reports. Power BI Service is a cloud-based platform for publishing reports, scheduling refreshes, managing permissions, and collaborating with team members. Desktop focuses on creation, while Service focuses on sharing, monitoring, and teamwork.
8. How does Power BI ensure secure access to sensitive data?
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Power BI uses Row-Level Security (RLS) to limit access based on user roles. Filters can control which rows of data a user can see, based on criteria like region, department, or business unit. When reports are published to Power BI Service, RLS guarantees that users only access the data they are authorized to view, protecting confidential information.
9. What is the difference between Import mode and DirectQuery mode in Power BI?
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Import mode loads data into Power BI for fast performance and offline access but needs scheduled refreshes to stay updated. DirectQuery mode keeps data in the source system and queries it live, providing real-time insights for large or frequently changing datasets, though performance may be slower. The choice depends on dataset size, update frequency, and reporting requirements.
10. How can the performance of a complex Power BI dashboard be improved?
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Dashboard speed can be optimized by simplifying DAX formulas, removing unused columns, and limiting the number of visuals per page. Using aggregated tables, fine-tuning queries, and enabling query folding further enhances efficiency. These techniques ensure dashboards are responsive, fast, and provide accurate insights for users.