Start by clarifying the table schema and the exact meaning of each assumption, then write a separate SQL query for each validation check that returns the violating rows. Use window functions and aggregation to detect duplicates, invalid values, and ordering violations, and explain how each query maps to the assumption.
Pro tip: Mention that you would run these checks as part of a data quality pipeline and set up alerts for violations, showing you think about production monitoring rather than one-off analysis.
Confirm column names, data types, and the precise definition of each assumption (e.g., what 'status_date' represents, whether it includes time). This ensures your queries target the right columns and logic.
Write a query that selects rows where status is not 'active' or 'inactive'. Use a simple WHERE clause with NOT IN to return all violating rows.
Use GROUP BY with HAVING COUNT(*) > 1 to find duplicate combinations, then join back to the original table to return the full violating rows.
Use the LAG window function partitioned by subscription_id ordered by status_date to compare each row's date with the previous one. Return rows where the current date is not greater than the previous date.
Group by subscription_id and DATE(status_date) and use HAVING COUNT(*) > 1 to find violations, then join back to return the actual rows. This is similar to step 3 but at the day level.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you've done the data quality part.
First, clarify the table schema and the meaning of 'latest known status' as of the reference date 2025-09-01. Then use a window function like ROW_NUMBER() partitioned by subscription_id and ordered by effective_date descending to pick the most recent status on or before that date. Finally, return subscription_id, status, and effective_date for the top-ranked row per subscription.
Pro tip: Explicitly state your assumption that the table has subscription_id, status, and effective_date columns, and that status changes are recorded as new rows with effective dates. This shows you think about data model semantics before writing SQL, which is exactly what Amazon values in data scientists.
Confirm the table columns (subscription_id, status, effective_date) and that each row represents a status change effective from that date. Ask whether future-dated statuses exist and should be excluded.
Apply a WHERE clause to keep only rows where effective_date <= '2025-09-01', ensuring you only consider statuses known by the reference date.
Use ROW_NUMBER() OVER (PARTITION BY subscription_id ORDER BY effective_date DESC) to assign rank 1 to the latest status for each subscription.
Wrap the ranked query in a CTE or subquery and filter for rank = 1, then select subscription_id, status, and effective_date.
Mention handling ties (e.g., same effective_date) and subscriptions with no status before the reference date, and confirm the output format.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Filtering out unexpected statuses before the groupby is the part people probably miss.
Start by clarifying the data schema and edge cases, then outline a pandas solution using groupby and conditional aggregation. Emphasize handling duplicates, unexpected statuses, and nulls to ensure robustness. Finally, discuss validation and performance considerations.
Pro tip: Mention that you would first inspect the data distribution and status values to catch unexpected statuses early, and use vectorized operations instead of apply for scalability.
Confirm the input DataFrame structure, expected status values, and how to handle duplicates and nulls. Ask about the definition of 'most recent' and whether dates are inclusive.
Handle duplicates by dropping or aggregating, filter out unexpected statuses, and decide on null handling (e.g., drop rows with null status_date or status).
Filter for status == 'active', then group by subscription_id and compute the minimum status_date. Use groupby and min aggregation.
Filter for status == 'inactive' and status_date <= '2025-09-01', then group by subscription_id and compute the maximum status_date.
Merge the two results on subscription_id with an outer join, and fill missing dates with NaT or a sentinel value. Validate the output.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, aggregate the orders DataFrame by customer to compute total order count and total order amount. Then apply a boolean condition to filter customers where order count < 2 OR total amount < 100.00, and return the list of customer IDs.
Pro tip: Clarify whether 'total order amount' includes cancelled or refunded orders, and mention that you'd handle ties or missing data appropriately. Also, consider using a single groupby.agg to compute both metrics efficiently.
Inspect the orders DataFrame to identify columns for customer ID, order ID, and order amount. Clarify any ambiguities like time range, order status, and currency.
Group by customer ID and compute the total number of orders (count of order IDs) and the total order amount (sum of order amounts).
Create a boolean mask where total orders < 2 OR total amount < 100.00, and select customers that satisfy either condition.
Extract the customer IDs from the filtered DataFrame. Optionally, validate by checking edge cases (e.g., customers with exactly 2 orders and exactly 100.00 total).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The tie-breaking rules are where this gets annoying.
Use a SQL query with window functions to rank customers within each month by order count and total amount, applying the specified tie-breakers. Then filter to top 5 per ranking per month, ensuring all customers are returned if fewer than 5 exist.
Pro tip: Clarify whether 'order count' means distinct orders or total items, and confirm that tie-breaking applies to both leaderboards. Also, mention that using ROW_NUMBER() with proper partitioning and ordering handles ties deterministically.
Compute total order count and total order amount for each customer in each month using GROUP BY on customer and month.
Use window functions (e.g., ROW_NUMBER() or RANK()) partitioned by month, ordered by the metric descending, then total amount descending, then cust_id ascending.
Select rows where the rank is <= 5 for each ranking type, ensuring that if fewer than 5 customers exist, all are included.
Union or join the two leaderboards, labeling each row with the leaderboard type (order count or total amount) for clarity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, aggregate total spend per customer and product type using a GROUP BY or conditional aggregation. Then pivot the result into a wide format using conditional SUM (CASE WHEN) or a pivot function, ensuring all product types become columns and missing values are filled with 0.00. Finally, validate the output for correctness and completeness.
Pro tip: Explicitly handle NULLs with COALESCE or IFNULL to avoid unexpected NULLs in the final output, and consider performance implications of pivoting large datasets by filtering early or using efficient aggregation.
Use a GROUP BY on customer ID and product type to sum the spend, or use conditional aggregation if pivoting directly. Ensure you handle any NULLs in spend appropriately.
Transform the long format into wide format using conditional SUM (CASE WHEN product_type = 'camera' THEN spend ELSE 0 END) for each product type, or use a PIVOT function if supported by your SQL dialect.
After pivoting, any customer-product combination that had no spend will result in NULL. Use COALESCE or IFNULL to replace NULLs with 0.00, ensuring the output matches the required format.
Check that all customers are included and that the columns are exactly cust_id, camera, shoes, laptop, clothes. Ensure numeric values are properly formatted to two decimal places.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.