Clarify the schema and definitions (e.g., how refunds are represented, date range) before writing SQL. Then compute net revenue per vendor by aggregating purchases and refunds separately, joining to vendors, filtering to the last two years, and ranking to get the top 3. Finally, discuss edge cases and validation.
Pro tip: Always confirm whether 'net revenue' should be calculated per transaction or as a sum of purchases minus sum of refunds; the latter is more common but can yield different results if refunds are not linked to specific purchases. Also, consider using a CTE for readability and to avoid repeating the date filter.
Ask about the table structures, how refunds are recorded (e.g., negative amounts, separate rows, or a transaction type column), and the exact definition of 'past two years' (e.g., relative to current date or a fixed period).
Write a query that aggregates purchases and refunds separately (or uses conditional aggregation) to calculate total net revenue for each vendor, ensuring proper handling of refunds (e.g., subtracting them).
Apply a date filter to include only transactions from the last two years, using appropriate date functions and considering time zones if relevant.
Order vendors by net revenue descending and limit to the top 3, using either LIMIT or a window function like RANK() if ties need special handling.
Mention potential issues such as vendors with no transactions, refunds without matching purchases, or currency conversion, and suggest ways to validate results (e.g., sanity checks).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the schema and definitions (e.g., net revenue, time range, ties) first, then outline a SQL solution using filtering, aggregation, and window functions (ROW_NUMBER) to rank vendors per state-city group. Discuss performance considerations like indexing and partitioning for large datasets.
Pro tip: Mention how you'd handle ties in revenue (e.g., using RANK vs. ROW_NUMBER) and the importance of confirming whether 'top 3' should include ties or exactly three vendors.
Ask about the schema, definition of net revenue, the exact two-year period, and how to handle ties or missing data.
Filter transactions to the last two years, then group by state, city, and vendor to compute total net revenue.
Use a window function like ROW_NUMBER() or RANK() partitioned by state and city, ordered by total net revenue descending.
Filter the ranked results to keep only rows where the rank is 3 or less, ensuring the top 3 vendors per group.
Discuss indexing, partitioning, and query plan analysis; validate results with edge cases like ties or sparse data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the most interesting part and also where I spent the most time.
Start by categorizing data quality checks into referential integrity, uniqueness, consistency, and statistical anomalies. For each category, propose specific checks and write clear SQL queries that detect violations, using joins, aggregations, and window functions. Prioritize checks that address the explicitly mentioned issues and explain the rationale behind each.
Pro tip: Mention that anomaly detection thresholds (e.g., for volume spikes) should be configurable and ideally based on historical baselines, not hardcoded, to adapt to seasonality and business changes.
Break down the problem into referential integrity (e.g., vendor existence), uniqueness (e.g., duplicate transaction IDs), consistency (e.g., refund amount matching original), and statistical anomalies (e.g., volume/amount spikes).
For each category, list concrete checks: invalid refund references, mismatched refund amounts, duplicate transaction IDs, vendors not in vendor table, and unusual spikes in volume or amount.
Craft SQL queries using appropriate techniques: LEFT JOIN for referential integrity, GROUP BY/HAVING for duplicates, self-joins for refund consistency, and window functions or subqueries for anomaly detection.
Walk through each query, clarifying what it detects and how to interpret the output, including any assumptions about the schema.
Address performance considerations (e.g., indexing, partitioning) and how to handle large datasets, as well as potential false positives in anomaly detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.