## 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?

InterviewDB Experience

Transaction Audit: Detect Anomalous Transactions Exceeding Daily Limits

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Interview Experience

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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About This Question

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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Coding Sql Phone Onsite

About Brex Interview Reports

This question was reported by a candidate who interviewed at Brex. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Brex are the higher-signal extractions to take from this report.

For broader preparation context, the Brex interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Brex reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Brex Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Brex reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.