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Use a correlated subquery or a lateral join to find the most recent FX rate on or before each transaction date, then join back to the transactions table. Ensure the output includes all required columns and a flag indicating missing rates, handling NULLs appropriately.
Pro tip: Mention that you would validate the join by checking for duplicate transactions and ensuring the FX rate date is the maximum date <= transaction date, and consider performance implications of indexing the FX rate table on (currency, date).
Identify the transaction table with columns: transaction_id, date, customer, product, region, category, segment, amount, currency. Identify the FX rate table with columns: currency, date, rate_to_usd. Clarify that the most recent rate on or before the transaction date should be used.
Use a LEFT JOIN with a subquery that selects the maximum FX date <= transaction date for each transaction's currency, or use a lateral join to fetch the latest rate. Ensure the join condition includes currency and date <= transaction date.
Multiply the transaction amount by the FX rate to get USD amount. Use a CASE statement to flag missing rates (e.g., when FX rate is NULL, set flag to 1 or 'Missing', else 0 or 'Present').
Include transaction_id, date, customer, product, region, category, segment, USD amount (rounded appropriately), and the missing rate flag. Ensure column names are clear.
Check for duplicates, ensure correct handling of edge cases (e.g., transactions before earliest FX rate), and suggest indexing on FX table (currency, date) for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'don't hardcode months' part is what makes this annoying.
Start by clarifying the data schema and business context, then outline a SQL-based pivot using conditional aggregation with dynamically derived months. Emphasize data quality checks and performance considerations, and conclude with how you'd validate the report against source data.
Pro tip: Mention that you'd use a CTE to first extract distinct months and then join or filter, ensuring the report automatically adapts to the latest three months without code changes. Also, highlight the importance of handling NULLs and ensuring totals reconcile.
Ask about the source tables, column names, date formats, and whether the three-month window is based on the latest data or a specific period. Confirm the definition of 'total column' (e.g., sum across regions).
Use a subquery or CTE to select the three most recent distinct months from the data, ensuring they are ordered and limited. This avoids hardcoding and adapts to new data.
Write a query that groups by product category and uses CASE statements or FILTER clauses to sum revenue for each dynamically derived month, aliasing columns appropriately. Include a total column summing across regions.
Check for NULLs, ensure totals match, and consider indexing or partitioning for performance. Discuss how you'd test the query with sample data and handle edge cases like missing months.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tie-breaking with two criteria in a window function is one of those things that sounds easy until you write it out.
Start by clarifying the schema and assumptions (e.g., single transactions table with customer_id, region, transaction_date, amount_usd). Then outline a SQL solution using a window function to rank customers per region by total revenue, with tie-breakers on max transaction amount and customer_id. Finally, discuss edge cases like ties, missing data, and performance considerations.
Pro tip: Mention that you would validate the result by checking for ties and ensuring the tie-breaking logic is correctly applied, and consider using a CTE for readability and maintainability.
Ask about the table structure, column names, and whether revenue is already in USD. Confirm the definition of 'top customer' (e.g., by total revenue) and tie-breaking rules.
Write a subquery or CTE to sum transaction amounts for each customer within the target month and region, also capturing the maximum individual transaction amount.
Use a window function like ROW_NUMBER() with PARTITION BY region ORDER BY total_revenue DESC, max_transaction DESC, customer_id ASC to assign ranks.
Filter the ranked results to only include rows where rank = 1, returning region and customer_id (and possibly revenue for context).
Address handling of ties, nulls, and large datasets; suggest indexing on region, transaction_date, and customer_id for efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 30-day window is a tighter constraint than the earlier task, which just said 'on or before.' So a transaction could have a valid rate for the point-in-time join but still fail this check if the rate is older than 30 days.
Start by clarifying the data model: transactions table with currency and date, and an FX rates table with currency pairs and dates. Then describe a SQL-based approach using a LEFT JOIN or NOT EXISTS to find transactions where no rate exists in the 30-day window. Finally, outline a production monitoring strategy with automated checks, alerting, and dashboards.
Pro tip: Mention that you would exclude USD transactions early to avoid unnecessary computation, and that you would consider using a calendar table to handle weekends/holidays when FX rates might not be published.
Identify the transactions table (with transaction_id, currency, transaction_date) and the FX rates table (with currency_pair, rate_date, rate). Confirm that 'non-USD currencies' means the transaction currency is not USD, and that the FX rate needed is for converting that currency to USD (or another base).
Use a LEFT JOIN or NOT EXISTS to find transactions where no FX rate exists for the same currency within the 30 days prior to the transaction date. Ensure the date range is inclusive of the transaction date minus 30 days up to the day before the transaction date (or including it, depending on business rules).
Write a query that filters out USD transactions, then checks for missing rates. Use a subquery or CTE to pre-filter FX rates to the relevant date range for efficiency. Consider indexing on currency and date columns.
Set up a daily or hourly job that runs the query and alerts if any transactions are found. Use a dashboard to visualize gaps over time, and track metrics like count of missing rates per currency. Implement data quality checks and SLA for resolution.
Address weekends/holidays by using a calendar table or business day logic. Define a process for backfilling missing rates or flagging transactions for manual review. Consider fallback rates or interpolation if allowed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.