Break the problem into stages: first identify ACH credits and their associated returns, then use window functions to find rolling 24-hour windows with at least 3 credits and at least one return within 5 days, and finally join to compute net exposure. Use a subquery or CTE to calculate rolling counts and filter for the earliest qualifying window per user, then join to the transaction table to compute the net exposure.
Pro tip: Clarify the definition of 'rolling 24-hour window'—whether it's a fixed window (e.g., calendar day) or a sliding window based on transaction timestamps—and confirm the return window logic (e.g., return within 5 days of the credit). This shows attention to detail and avoids misinterpretation.
Filter the transaction table to ACH credits and identify returns associated with those credits, ensuring you capture the credit timestamp and return timestamp for each.
For each user, use a window function to count ACH credits within a rolling 24-hour window ending at each credit timestamp, and check if any of those credits have a return within 5 days.
Filter windows where the credit count >= 3 and at least one return exists within 5 days, then select the earliest window per user based on the window end timestamp.
Join the qualifying window back to the transaction table to sum credit amounts and debit amounts between the window start and the earliest return timestamp, then calculate net exposure as total credits minus total debits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cleaner than the first question but the 'most recent login within 60 minutes before the credit' part requires a lateral join or a correlated subquery and I blanked on the cleanest way to write it.
Break the problem into two parts: first, identify the most recent login within 60 minutes before each returned ACH credit transaction to link devices to credits; then, count these linked credits per device per user in the last 30 days and rank devices using a window function. Finally, filter to the top-ranked device per user and return the fingerprint and count.
Pro tip: Clarify the definition of 'returned ACH credit' (e.g., status = 'returned') and ensure you handle ties in ranking by specifying a tie-breaking rule (e.g., most recent login time or device fingerprint). Also, consider time zone consistency and whether the 30-day window is based on transaction date or current date.
Select credit transactions that are returned and occurred within the last 30 days from the current date (or a specified reference date).
For each returned credit, find the login event for the same user that occurred within 60 minutes before the transaction and is the most recent. Use a lateral join or window function to pick the latest login.
Group by user and device fingerprint to count the number of linked returned credits.
Use ROW_NUMBER() or RANK() partitioned by user, ordered by count descending (and a tie-breaker if needed) to assign a rank to each device.
Filter to rows where rank = 1 and return user identifier, device fingerprint, and the count.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew union-find going in but combining it with the streaming late-event handling and the 7-day sliding window on top made this genuinely hard to finish in one session.
Start by clarifying the problem and outlining a streaming architecture that handles late and out-of-order events using a watermark and allowed lateness. Then detail the union-find clustering with a deterministic canonical selection rule, and explain the sliding window aggregation for distinct user counts. Finally, analyze time and space complexity and discuss trade-offs.
Pro tip: Mention that you would use a deterministic tie-breaker for canonical fingerprint (e.g., lexicographically smallest) to ensure reproducibility, and that you would handle late events by maintaining a buffer with a watermark and recomputing affected windows.
Ask about event schema, definition of 'differ by exactly one character', expected throughput, and whether the 7-day window is sliding or tumbling. Confirm that canonical fingerprint is the representative of a cluster.
Use a watermark with allowed lateness (e.g., 5 minutes) and a buffer to hold out-of-order events. For each event, update the union-find structure and maintain per-fingerprint user sets with timestamps.
For each new fingerprint, compare with existing fingerprints that differ by one character (using a hash map of patterns) and union them. Choose canonical fingerprint deterministically (e.g., lexicographically smallest) and update cluster metadata.
For each cluster, maintain a time-ordered list of (timestamp, user) events. Use a sliding window of 7 days ending 2025-09-01 to count distinct users, emitting tuples when count >= 3.
State time complexity: near O(N α(N)) for union-find plus O(N log N) for windowing; space O(N). Discuss trade-offs between exactness and memory, and how late events trigger recomputation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.