In this Uber interview, the candidate was asked to design a system to manage customers, their revenues, and referral relationships. Each customer generates revenue for the company and can refer others, which in turn increases overall revenue. The system also needed to support a special query: finding the lowest k customers by total revenue, but only among those who meet a minimum threshold.
The problem was stated as follows:
int insertNewCustomer(double revenue)→ insert a new customer, return an auto-incremented ID (0-based).int insertNewCustomer(double revenue, int referrerID)→ insert a new customer with a referrer, return ID.Set<Integer> getLowestKCustomersByMinTotalRevenue(int k, double minTotalRevenue)→ return the IDs of the k customers who contributed the least revenue, provided their total revenue is still ≥ the threshold.
At the very beginning, the candidate seemed unsure about how to start, but CSOahelp immediately pushed out a checklist of clarifications to reproduce: Should total revenue include the entire referral chain? Could revenue ever be negative? What happens if k is larger than the number of eligible customers? How do we handle cases where no one meets the threshold? Simply by repeating these questions to the interviewer, the candidate came across as systematic and thorough.
When it came to modeling, CSOahelp streamed the full framework in real time: a Customer object storing ID, own revenue, total revenue, referrer, and list of referrals, and a CustomerManager to assign IDs, insert new customers, recursively update totals, and run queries. The candidate echoed this structure back smoothly, as though it was their own line of reasoning.
As the interviewer probed on the query method, CSOahelp instantly delivered the baseline solution: filter all customers above the threshold, sort them by total revenue, and return the first k. That’s O(n log n). The candidate repeated this cleanly, then, with CSOahelp feeding additional points, added optimization strategies: for large n and small k, use a max-heap of size k to reduce complexity to O(n log k); or for general cases, push all eligible customers into a min-heap and pop k times for O(n + k log m). The interviewer was clearly impressed that the candidate could move from a simple approach to a scalable one without hesitation.
Edge cases were also covered effortlessly. What if a pointer to a customer ID didn’t exist? What if there were no customers yet? What if the threshold was too high and no one qualified? Each time, CSOahelp provided a complete, annotated answer in real time, and the candidate simply relayed it back with confidence.
By the end, the interviewer had heard a full, layered solution: from clarification to object modeling, from naive sorting to heap optimization, from complexity analysis to edge case handling. To the interviewer, it felt like the candidate had built and refined the solution step by step on the spot. In reality, CSOahelp was delivering those complete answers in real time, ensuring the candidate never stalled or left gaps.
That’s the core value of CSOahelp. We don’t hand you a script in advance. We support you live, pushing out full solutions with reasoning, trade-offs, and edge cases, so you can focus on delivering them smoothly under pressure. For candidates aiming at companies like Uber, Google, or Amazon, this kind of real-time backup can mean the difference between stumbling and standing out.
If you’re preparing for algorithm and system design interviews at Uber,Apple, Meta, TikTok, etc., but aren’t sure how to break down problems and handle edge cases, get North American interview secrets. We also offer interview proxy, interview assistance, OA writing, and more to help you land your dream job sooner~
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