## Problem You are given a list of transactions `(account_id, timestamp, amount)`. Flag any account where the total transaction amount exceeds a daily limit `L` on any single calendar day. Return a list of `(account_id, date)` pairs that were flagged, sorted by account then date. ```python from datetime import date def flag_accounts( transactions: list[tuple[int, int, float]], # (account_id, unix_ts, amount) daily_limit: float ) -> list[tuple[int, date]]: pass ``` ``` Input: transactions = [ (1, 1700000000, 500.0), (1, 1700000100, 600.0), # same day as above (2, 1700000000, 100.0), ] daily_limit = 1000.0 Output: [(1, date(2023, 11, 14))] # Account 1 total on that day = 1100 > 1000 # Account 2 total = 100 <= 1000 ``` ## Follow-ups 1. How do you handle timezone-aware vs. UTC timestamps when grouping by calendar day? 2. If transactions stream in real time, how do you efficiently update daily totals without reprocessing all records? 3. Extend to flag accounts that exceed a rolling 24-hour window, not just a calendar day. 4. How would you write a SQL query to solve this problem instead?

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Transaction Audit: Detect Anomalous Transactions Exceeding Daily Limits

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Problem You are given a list of transactions (account_id, timestamp, amount). Flag any account where the total transaction amount exceeds a daily limit L on any single calendar day. Return a list of (account_id, date) pairs that were flagged, sorted by account then date. Follow-ups How do you handle timezone-aware vs. UTC timestamps when grouping by calendar day? If transactions stream in real time, how do you efficiently update daily totals without reprocessing all records? Extend to flag accou…

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This is a candidate experience report from a brex interview during the phone round.

It covers the following topics: Coding, Sql, Phone, Onsite .

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Transaction Audit: Detect Anomalous Transactions Exceeding Daily Limits

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