## Round 1 - Coding ## Problem Implement a Rock Paper Scissors game engine. Support two players (human or bot), track wins/losses/draws per player, and return a match summary after `N` rounds. ```python class RPSGame: MOVES = {"rock", "paper", "scissors"} def __init__(self, rounds: int): ... def play_round(self, move1: str, move2: str) -> str: # returns "player1", "player2", or "draw" ... def get_stats(self) -> dict: # {"player1": {"wins":int,"losses":int,"draws":int}, # "player2": {...}, "rounds_played": int} ... def winner(self) -> str: # "player1", "player2", or "tie" after all rounds ... ``` ## Example ``` game = RPSGame(rounds=3) game.play_round("rock", "scissors") -> "player1" game.play_round("paper", "paper") -> "draw" game.play_round("scissors", "rock") -> "player2" game.get_stats() # -> {"player1":{"wins":1,"losses":1,"draws":1}, # "player2":{"wins":1,"losses":1,"draws":1}, "rounds_played":3} game.winner() -> "tie" ``` ## Follow-ups 1. How would you add Rock Paper Scissors Lizard Spock? What data structure best represents the win/loss matrix? 2. Implement a simple bot strategy: always play the move that beat the opponent's last move. 3. How do you validate that moves are legal and handle invalid input gracefully? 4. How would you serialize and restore game state mid-match?

InterviewDB Question

Rock Paper Scissors: Implement a Multi-Round Game Engine with Stats Tracking

Onsite

Question Details

Round 1 - Coding Problem Implement a Rock Paper Scissors game engine. Support two players (human or bot), track wins/losses/draws per player, and return a match summary after N rounds. Example Follow-ups How would you add Rock Paper Scissors Lizard Spock? What data structure best represents the win/loss matrix? Implement a simple bot strategy: always play the move that beat the opponent's last move. How do you validate that moves are legal and handle invalid input gracefully? How would you seria…

Full Details

🔒

Unlock all Ramp questions

Full insider details, leaked discussions, and candidate experiences.

Get full access — from $50/mo

Topics

Coding Onsite Matrix

← All Ramp questions Ramp Software Engineer Questions → All Coding Other Questions →

About Ramp Interview Reports

This question was reported by a candidate who interviewed at Ramp. 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 Ramp are the higher-signal extractions to take from this report.

For broader preparation context, the Ramp 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 Ramp reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Ramp 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 Ramp 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.