← Robinhood Interview Insights
Start by clarifying the schema and definitions (e.g., declined transactions, user identification, date range). Then write a SQL query that filters transactions to the last 7 days, aggregates total declined amount per user, sorts descending, and limits to top 3. Use window functions or ORDER BY with LIMIT depending on SQL dialect.
Pro tip: Mention that you'd validate the date range using the transaction timestamp and consider timezone implications, especially for a financial app like Robinhood where transactions may span multiple timezones. Also, discuss how to handle users with no declined transactions (they should be excluded).
Ask about the table structure, column names, and definitions (e.g., what constitutes a declined transaction, how to identify users). Confirm the date range: last 7 days from current date or a specific end date?
Use a WHERE clause to select only declined transactions within the last 7 days. Ensure the date filter uses the appropriate timestamp column and handles timezone if needed.
Group by user ID and sum the transaction amounts to get total declined amount per user. Use SUM(amount) and GROUP BY user_id.
Order the results by total declined amount descending and limit to the top 3 users. Use ORDER BY total_declined DESC LIMIT 3.
Discuss handling ties (e.g., using RANK or DENSE_RANK if ties matter), indexing on date and user_id for performance, and whether to include additional user details via JOIN.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a window function to compute rolling counts of risky transactions (amount IS NULL OR amount > 500) over a 24-hour window per user, then filter to windows where the count exceeds 2 and flag them. Ensure the window is defined as RANGE BETWEEN INTERVAL '24 hours' PRECEDING AND CURRENT ROW on the transaction timestamp, and handle ties or duplicate timestamps appropriately.
Pro tip: Clarify the definition of '24-hour rolling window'—whether it's a fixed window (e.g., calendar day) or a sliding window—and confirm the handling of NULL amounts as risky. Also, consider performance implications and suggest indexing on (user_id, transaction_time) for large datasets.
Clarify that a risky transaction is one where amount IS NULL OR amount > 500. A 24-hour rolling window means any consecutive 24-hour period, not necessarily aligned to calendar days. The output should include user_id, window_start (the start of the 24-hour window), and risky_flag (e.g., 1 if more than 2 risky transactions in that window).
Filter or flag transactions where amount IS NULL OR amount > 500. This can be done with a CASE statement or a WHERE clause, depending on whether you need all transactions for the window count.
Use a window function like COUNT(*) OVER (PARTITION BY user_id ORDER BY transaction_time RANGE BETWEEN INTERVAL '24 hours' PRECEDING AND CURRENT ROW) to count risky transactions in the preceding 24 hours for each transaction. Alternatively, use a self-join or a windowed aggregation with a subquery.
Select rows where the rolling count > 2. For each such row, the window_start can be defined as the transaction_time minus 24 hours (or the earliest transaction in the window). Set risky_flag = 1 for these windows.
Ensure the output includes user_id, window_start, and risky_flag. Consider deduplication if multiple overlapping windows qualify, and decide whether to return all qualifying windows or just the first per user per day. Also, handle users with no risky transactions (they should not appear or appear with flag 0, depending on requirements).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Write a SQL query that uses a CASE statement to assign risk levels based on amount thresholds, then order the results by timestamp descending and limit to 10 rows. Clearly explain the conditional logic and ensure the output includes the new risk_level column.
Pro tip: Mention that you would validate the thresholds with stakeholders to ensure they align with business definitions of risk, and consider edge cases like exactly 500 or 800. Also, note that ordering by timestamp descending ensures you're looking at the most recent transactions, which is often critical for monitoring.
Clarify the table schema, especially the amount and timestamp columns, and confirm the exact thresholds for risk levels. Ensure you know whether the boundaries are inclusive or exclusive.
Use a CASE expression to categorize amounts: WHEN amount > 800 THEN 'high', WHEN amount BETWEEN 500 AND 800 THEN 'medium', ELSE 'low'. Be mindful of boundary conditions (e.g., 800 should be medium).
Select all relevant columns plus the new risk_level, order by timestamp descending to get the most recent transactions, and limit the output to 10 rows.
Run the query, check for correctness (e.g., no NULLs in risk_level), and be prepared to explain the logic and any assumptions made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.