Start by clarifying the schema and assumptions (e.g., date column, amount column, vendor ID). Then write a query that joins transactions and vendors, filters transactions to the last 2 years, groups by vendor, sums the amounts, orders descending, and limits to 3. Explain each clause and consider edge cases like ties or missing vendors.
Pro tip: Mention that you would confirm the definition of 'past 2 years' (e.g., rolling 24 months vs. calendar years) and whether to include vendors with zero transactions. This shows attention to detail and business context.
Ask about the table structures, column names, and the exact meaning of 'past 2 years' (e.g., from today or from the start of the year). Confirm if ties should be handled or if exactly 3 vendors are needed.
Use a WHERE clause to select only transactions within the last 2 years, using an appropriate date function (e.g., DATE_SUB(CURRENT_DATE, INTERVAL 2 YEAR) or equivalent).
Join the filtered transactions with the vendors table on vendor ID, then GROUP BY vendor and compute SUM(amount) as total_charged.
ORDER BY total_charged DESC and LIMIT 3 to get the top 3 vendors. If ties are possible, consider using DENSE_RANK() or explain how to handle them.
Check for correctness (e.g., no duplicate vendors due to multiple joins) and mention potential indexes on date and vendor_id for performance.
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., what 'amount charged' means, how to handle time zones, and whether to include all states/cities). Then write a SQL query that filters transactions to the last 2 years, groups by state, city, and vendor, sums the amounts, and uses a window function (ROW_NUMBER) to rank vendors within each location, finally filtering to top 3 per group.
Pro tip: Mention that you would verify the query's performance and correctness by checking indexes on date and location columns, and consider using a subquery or CTE to avoid repeating the date filter. Also, discuss how you would handle ties (e.g., using RANK vs DENSE_RANK) and whether to include vendors with zero charges.
Ask about the table structure, definitions of 'amount charged', time zone handling, and whether 'top 3' should be based on total amount or another metric. Confirm if states/cities with no vendors should appear.
Write a subquery or CTE that filters transactions to the past 2 years (using a date function like DATE_SUB or INTERVAL) and groups by state, city, and vendor, summing the charged amounts.
Use a window function (e.g., ROW_NUMBER() OVER (PARTITION BY state, city ORDER BY total_amount DESC)) to assign a rank to each vendor within each state-city group.
Wrap the ranked query in an outer query and filter to rows where rank <= 3. Optionally, order the final result by state, city, and rank for readability.
Address handling ties (use RANK or DENSE_RANK if needed), null values, and performance considerations such as indexing on date and location columns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Did not see this coming as a SQL question.
Start by clarifying the dataset's schema, business context, and what 'quality' means for Zoox (e.g., sensor data, trip logs). Then propose a layered set of checks covering completeness, validity, consistency, and anomaly detection, and implement them as SQL queries with clear thresholds and alerting.
Pro tip: Frame your checks as a monitoring system with severity levels and ownership, not just one-off queries. Mention how you'd integrate with CI/CD or observability tools to catch issues early.
Ask about the dataset's purpose, key columns, and expected patterns. Define quality dimensions like completeness, accuracy, consistency, timeliness, and uniqueness relevant to Zoox's autonomous driving data.
For each dimension, outline concrete checks (e.g., null counts, range violations, duplicate records, referential integrity). Prioritize checks based on business impact and data volume.
Write SQL queries for each check, using aggregations, window functions, and joins. Include thresholds and output formats (e.g., flag rows, summary counts) that can feed into alerts.
Go beyond static rules: use statistical methods (z-score, IQR) or time-series anomaly detection (e.g., comparing to moving averages) in SQL to catch outliers and sudden shifts.
Explain how to schedule these checks, route alerts, and handle false positives. Suggest monitoring dashboards and periodic review of thresholds as data evolves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.