1. How can Power BI be defined and what main parts does it include?
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Power BI is a Microsoft business intelligence tool used for connecting to data sources, modeling information and creating interactive dashboards and reports. Its main components are Power BI Desktop for building reports, Power BI Service for publishing and sharing in the cloud and Power BI Mobile for viewing insights on smartphones and tablets. Together, these elements support end-to-end data analysis.
2. In what way does Power Query help prepare and transform data in Power BI?
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Power Query helps clean and reshape raw information into a format suitable for reporting. It connects to different data sources and allows actions like filtering records, removing duplicates, changing data types, splitting or merging columns and combining tables. All these steps are performed through an easy interface, making data preparation efficient without coding.
3. What does DAX mean in Power BI and how are SUM and SUMX different?
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DAX, short for Data Analysis Expressions, is the formula language used to create custom calculations such as measures, calculated columns and tables. The SUM function totals values in a single column, while SUMX evaluates each row of a table with an expression and then adds the results. This makes SUMX more suitable for advanced or conditional aggregations.
4. What does a data model in Power BI represent and why is it significant?
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A data model in Power BI organizes tables, relationships, calculations and metadata into a structured layout. It allows data from multiple sources to work together as a unified system, enabling accurate analysis and smooth reporting. A well-designed model ensures performance, consistency and clear insight generation.
5. How are relationships created between tables in Power BI?
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Relationships are formed by linking tables through matching fields such as primary keys, IDs or codes. These connections can be built manually or automatically detected by Power BI. The most common setup is a one-to-many relationship, where one record in a main table connects to several related records in another table, ensuring data flows correctly across visuals.
6. What are calculated columns in Power BI and how are they different from measures?
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Calculated columns are created using DAX and compute values for each row, storing the results directly in the data model. These columns can be used in visuals, filters and relationships. Measures, however, calculate results dynamically based on filters applied in a report and are mainly used for aggregations or summarizing data.
7. What separates Power BI Desktop from Power BI Service and what roles do they play?
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Power BI Desktop is used for connecting to data, transforming it and designing reports with various visual elements. Power BI Service, the cloud platform, is used to publish those reports, share them with teams, schedule refreshes and manage permissions. Desktop focuses on building reports, while the Service is meant for distribution and collaboration.
8. How can data access be restricted for certain users in Power BI?
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Row-Level Security (RLS) is used to limit what data each user can see. By defining roles and applying filters on specific fields, the system ensures that individuals only view information relevant to their position, such as a regional manager seeing only their assigned territory. This method enhances security and personalized access.
9. What is the difference between DirectQuery and Import mode in Power BI and when should each be used?
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Import mode brings data into Power BI’s own storage, resulting in faster performance but requiring scheduled refreshes to stay up to date. DirectQuery keeps the data in its original source and retrieves it in real time whenever the report loads. Import is best for small to medium datasets, while DirectQuery suits large or frequently updated data environments.
10. Describe a situation where a complex dashboard was built and performance issues were resolved.
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A dashboard combining sales, stock and customer data from multiple sources faced performance slowdowns due to heavy visuals and intricate DAX calculations. Improvements were made by reducing unnecessary visuals, simplifying the data model, removing unused columns and switching some calculated columns to measures. Additional optimization came from using pre-aggregated summary tables, resulting in a much faster and smoother report.