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Tableau, Power BI, and Looker Fundamentals Questions

Fundamental concepts and usage patterns for leading business intelligence and data visualization tools Tableau, Power BI, and Looker. Covers connecting to data sources, data modeling for visual dashboards, creating and sharing interactive dashboards and reports, using built-in chart types, calculations and measures, filters and parameters, data governance basics, and best practices for data storytelling and actionable analytics.

MediumTechnical
91 practiced
Write an ANSI SQL query to compute a running monthly revenue total for the last 12 months by month and by customer. Given orders(order_id INT, user_id INT, amount DECIMAL, order_date DATE), output: user_id, year_month, monthly_revenue, running_total. Use window functions and ensure correct partitioning and ordering.
MediumTechnical
84 practiced
Explain how to implement row-level security (RLS) in Tableau, Power BI, and Looker for a multi-tenant application where users should only see data for their organization. Cover tool-native RLS, database-level RLS, techniques for mapping users to tenants, and how SSO attribute passing can simplify enforcement.
HardTechnical
79 practiced
A product manager notifies you that conversion rate has declined 12% month over month. Describe your end-to-end investigation plan: quick checks to rule out tracking or ETL bugs, queries to run, dashboards to build (funnel, cohort, traffic source), hypotheses to prioritize, and a communication plan for stakeholders.
MediumTechnical
66 practiced
You receive a dataset with missing values for product_category and many NULL prices. As a BI analyst building dashboards, describe strategies you would use to handle missing categorical and numeric data in Tableau/Power BI/Looker, including imputation, bucketing, user communication, and filtering. Explain pros and cons of each approach.
EasyTechnical
66 practiced
Given a dataset with revenue and cost columns, write the calculated-field formula to compute profit margin in Tableau, the equivalent DAX measure in Power BI, and the LookML measure in Looker. Explain where each calculation is evaluated (data source, semantic layer, or visualization layer) and how evaluation location affects performance.

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