Straightforward aggregation, sum the amount column from transactions, group by pay_method, order descending, limit 5.
Start by clarifying the schema and the definition of 'total sales amount' (e.g., sum of transaction amounts). Then write a SQL query that joins the transactions table with the payment methods (likely a column in transactions), groups by payment method, sums the sales, orders descending, and limits to 5. Consider any necessary filters (e.g., completed transactions) and handle potential NULLs.
Pro tip: Mention that you would validate the results by checking for data quality issues (e.g., negative amounts, refunds) and consider if payment methods need to be normalized or if there are multiple payment methods per transaction. Also, discuss how you might handle ties in ranking.
Ask questions to confirm the table structures, especially which table contains payment method and sales amount. Clarify if 'total sales amount' means sum of transaction amounts or quantity sold, and if there are any filters (e.g., only completed transactions).
Determine that the transactions table likely contains payment_method and amount. If payment method is in another table, identify the join key. Usually, only the transactions table is needed, but if payment method is in a separate table, join accordingly.
Use GROUP BY on payment_method, SUM(amount) as total_sales, ORDER BY total_sales DESC, and LIMIT 5. Ensure proper handling of NULLs and consider using COALESCE if needed.
Discuss potential data issues: refunds (negative amounts), multiple currencies, or payment methods with no sales. Also, consider if ties should be handled (e.g., using RANK() window function).
Walk through the query step by step, explaining each clause and why it's necessary. Mention any assumptions made and how you would test the query.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Part (b) was easy, just a LIKE filter with COUNT.
Clarify the data schema and definitions (e.g., what constitutes an author, zero sales, and a personal URL) before writing any queries. Then use SQL to compute each proportion separately, ensuring you handle NULLs and edge cases appropriately. Finally, validate results with sanity checks and consider segmenting by relevant dimensions.
Pro tip: Always state your assumptions explicitly and confirm them with the interviewer—this shows you think like a data scientist who cares about data quality and business context. Also, mention that you would check for data completeness and potential biases in the dataset.
Ask clarifying questions to define 'author', 'zero sales', 'personal URL', and the specific keyword. Confirm the time frame and whether sales include all formats.
Identify relevant tables (e.g., authors, sales, profiles) and columns. Check for NULLs, duplicates, and data types.
Write SQL queries: (a) count authors with zero sales divided by total authors; (b) count authors with personal URL containing keyword divided by total authors. Use LEFT JOINs and careful filtering.
Verify results by checking totals, sampling records, and ensuring no double-counting. Consider if proportions make sense given domain knowledge.
Report the proportions clearly, mention any caveats, and suggest potential next steps or segmentations if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the schema and assumptions (e.g., how referring_customer_id links to customer_id, and that book purchases are in a separate table with price). Then, write a SQL query that joins customers to purchases, groups by referrer, computes the average price of books bought by referred customers, and orders descending to get the top 5.
Pro tip: Mention that you would handle ties carefully—if multiple referrers have the same average price, you might need to decide whether to include all or use a tiebreaker. Also, explicitly exclude rows where referring_customer_id is NULL to avoid counting non-referred customers.
Confirm the table structures: customers table has customer_id and referring_customer_id; purchases table has customer_id, book_id, and price. Assume each purchase is a book bought by that customer.
Filter customers where referring_customer_id IS NOT NULL to focus only on customers who were referred by someone.
Join the referred customers to the purchases table on customer_id, then group by the referrer (referring_customer_id) and compute AVG(price) for each referrer.
Order the grouped results by average price descending and limit to 5 referrers. Consider using a window function like RANK() or DENSE_RANK() if ties are a concern.
Check for referrers with no purchases (should be excluded), handle NULLs, and discuss how ties or missing data might affect the result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.