Start by clarifying the problem constraints and edge cases, then outline a brute-force solution before optimizing with the appropriate technique (DP, string manipulation, or greedy). Implement and test each solution incrementally, ensuring time and space complexity are analyzed and communicated throughout.
Pro tip: Verbally walk through your thought process and trade-offs before coding; interviewers value clear reasoning and the ability to adapt when hints are given, often more than a perfect solution.
Restate the problem in your own words, ask clarifying questions about input size, constraints, and edge cases, and confirm expected output format.
Discuss a brute-force solution and its complexity, then identify patterns (overlapping subproblems, optimal substructure, local optimality) that suggest DP, string algorithms, or greedy.
Choose the most efficient approach, define state or greedy criteria, and outline the algorithm step-by-step before writing code.
Write clean, modular code, then test with provided examples and edge cases, debugging aloud as you go.
State time and space complexity, discuss potential improvements or alternative solutions, and summarize key insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the most unusual round I've done.
Start by clarifying requirements and scoping down to a minimal viable product (MVP) that can be built in one hour. Choose a familiar framework (e.g., Node.js/Express, Python/Flask, or Ruby on Rails) and use rapid development tools like SQLite and JWT for authentication. Focus on core functionality: user registration/login, event creation, and booking with basic validation, while explaining design decisions and trade-offs.
Pro tip: Use an in-memory or file-based database (e.g., SQLite) to avoid setup overhead, and leverage framework generators or scaffolding to speed up development. Communicate your progress and decisions clearly, as the interviewer values your thought process as much as the final code.
Ask clarifying questions to understand must-have features (e.g., user roles, event capacity, booking constraints) and agree on an MVP scope that fits the time limit.
Sketch a simple database schema (users, events, bookings) and define RESTful endpoints (e.g., POST /register, POST /login, POST /events, POST /bookings).
Initialize the project with your chosen framework, set up the database, and implement authentication (e.g., JWT) with password hashing.
Code the main endpoints: user registration/login, event creation, and booking with validation (e.g., check event capacity, prevent double-booking).
Run quick tests (e.g., using curl or Postman) to demonstrate functionality, and explain your design choices, trade-offs, and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Left one requirement unfinished and explained verbally how I'd approach it.
Start by clarifying functional and non-functional requirements, then design a normalized schema for users, groups, expenses, and settlements. Explain the core expense-splitting logic (equal, exact, percentage, shares) and how balances are computed, and discuss trade-offs like using a ledger vs. materialized balances.
Pro tip: Emphasize idempotency and consistency in expense creation and settlement, as financial data requires strong guarantees; mention how you'd handle concurrency and auditability.
Ask about scale, supported split types, currencies, and whether real-time updates or offline support are needed. Confirm non-functional requirements like consistency and latency.
Propose tables for users, groups, group_members, expenses, expense_splits, and settlements. Include fields like amount, currency, payer_id, and timestamps, and discuss indexing for common queries.
Explain how to validate and store different split types, compute each participant's share, and update balances. Describe how to handle rounding and currency conversion.
Detail how to compute net balances per user per group, either on-the-fly or via materialized views, and how settlements reduce balances. Discuss simplifying debts with algorithms like minimum cash flow.
Compare normalized vs. denormalized approaches, discuss consistency models (strong vs. eventual), and how to scale reads/writes (caching, sharding, event sourcing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.