I went straight for RANK() partitioned by month and ordered by sum descending, which was the right instinct.
Start by clarifying the schema and edge cases, then outline a SQL solution using aggregation, window functions, and tie-breaking logic. Walk through the query step-by-step, explaining how to compute monthly category totals, rank them, and filter to the top 3.
Pro tip: Mention that you would validate the results by checking for ties and ensuring the ranking handles them correctly, and discuss how to optimize the query for large datasets using partitioning or indexing.
Ask about the table structure, data types, and any constraints. Confirm the definition of 'top 3' and tie-breaking rules.
Use GROUP BY on the month and category to compute total sales. Ensure date truncation to month level.
Apply a window function like ROW_NUMBER() or RANK() with ORDER BY total_sales DESC, category ASC, partitioned by month.
Use a subquery or CTE to select rows where the rank is <= 3, ensuring ties are handled correctly.
Check results for correctness, especially ties. Discuss indexing, partitioning, or other optimizations for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Running totals are pretty standard but I always blank on whether it's ROWS UNBOUNDED PRECEDING or just the default frame.
Clarify the requirements and edge cases, then outline a SQL solution using a window function to compute the running total per customer ordered by date. Explain the logic step-by-step, including how to handle ties and nulls, and validate with a small example.
Pro tip: Mention that you would verify the running total with a self-join or correlated subquery for correctness, and discuss performance implications of window functions on large datasets.
Ask about tie-breaking in order dates, handling of null amounts, and whether the running total should include the current order. Confirm the expected output format.
Recognize that a window function with PARTITION BY customer and ORDER BY order_date is needed to compute the cumulative sum. Mention SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date).
Construct the SQL query selecting order_id, order_date, amount, and the window function as running_total. Include necessary columns for partitioning and ordering.
Address ties by adding a secondary sort key (e.g., order_id) to ensure deterministic ordering. Consider using COALESCE for null amounts and specify ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW if needed.
Test with sample data to verify correctness. Discuss performance considerations, such as indexing on (customer_id, order_date) and the efficiency of window functions versus self-joins.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CASE WHEN wrapped in a COUNT GROUP BY, straightforward.
Start by clarifying the data schema and the exact meaning of 'amount' (e.g., order total, pre-tax, etc.). Then outline a SQL query using a CASE statement to classify orders and a GROUP BY with COUNT to aggregate, ensuring boundary conditions are handled correctly. Finally, discuss how you would validate the results and handle edge cases like NULLs or negative amounts.
Pro tip: Mention that you would use a CASE statement with explicit ranges and test boundary values (e.g., exactly 100 and 200) to avoid off-by-one errors. Also, note that you would consider using a subquery or CTE for readability and maintainability.
Ask clarifying questions about the data source, the definition of 'amount' (e.g., order total, item price), and whether NULLs or negative values are possible. Confirm the classification boundaries: High > 200, Medium 100-200 inclusive, Low < 100.
Use a CASE statement to assign each order to a category based on the amount. Ensure the conditions are ordered correctly (e.g., amount > 200, then amount >= 100, else Low) to avoid overlap.
Wrap the CASE statement in a subquery or CTE, then use GROUP BY on the category and COUNT(*) to get the number of orders in each classification.
Check for NULLs or unexpected values and decide how to handle them (e.g., exclude or classify as 'Unknown'). Test with sample data including boundary values (100 and 200) to ensure correct classification.
Show the final counts and briefly discuss any patterns or implications, such as the distribution of order sizes and potential business insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.