Start by clarifying the data model: identify the sales fact table and the customer dimension table with city information. Then write a SQL query that joins these tables, groups by city, and sums the sales amount, ensuring you handle any necessary filters or date ranges.
Pro tip: Mention that you would validate the results by checking for null cities and ensuring the total sales match the overall sum, as data quality issues can skew city-level breakdowns.
Ask about the time period, definition of 'sales' (e.g., revenue, units), and whether to include all cities or only those with sales. Confirm the table structures and join keys.
Determine the sales fact table (e.g., orders, transactions) and the customer dimension table containing city. Identify the common key, typically customer_id.
Construct a query that joins the tables, groups by city, and sums the sales amount. Use appropriate aggregation functions and aliases.
Consider filtering by date range if needed, handling NULL cities, and deciding whether to include cities with zero sales. Use LEFT JOIN if you want all cities.
Check that the total sales across cities matches the overall total. Discuss indexing or partitioning for performance if the dataset is large.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.