Compare two poker hands with possibly incomplete information (output UNKNOWN if winner cannot be determined)
Interview Experience
Problem: Compare two poker hands with possibly incomplete information (output UNKNOWN if undecidable)
You are given two players' poker hands. Each hand represents known card ranks (duplicates al
Full Details
Problem: Compare two poker hands with possibly incomplete information (output UNKNOWN if undecidable)
You are given two players' poker hands. Each hand represents known card ranks (duplicates allowed), but the input hand may be incomplete (i.e., some of the 5 final cards are unknown).
Determine the outcome based on the known information:
Output A if Player A must win regardless of how the missing cards are filled.
Output B if Player B must win regardless of how the missing cards are filled.
Output UNKNOWN if the winner cannot be determined uniquely (i.e., there exists a completion where A wins and another where B wins, or a tie/ambiguity is possible).
Note
- Per the interview description, each hand is ultimately 5 cards.
- The key twist is handling incomplete input.
Example: known 9999 vs 9 is not enough to determine the winner, so output UNKNOWN.
Input (you may define exact parsing)
- Two strings handA and handB, each containing 1 to 5 known ranks.
- Rank range / whether suits matter: not specified; you may treat ranks only.
Output
Print one of: A, B, UNKNOWN.
Constraints
- Final hand size is 5;
input size is 1..5.
Sample Tests
1) A=9999, B=9 => UNKNOWN
2) A=23456, B=99999 => B
3) A=AAAAA, B=KKKKK => A
4) A=2222, B=3333 => UNKNOWN
5) A=7, B=8 => UNKNOWN
Sample Input
9999
9
Sample Output
UNKNOWN
Test Cases
Case 1
Input:
9999
9
Output:
UNKNOWN
Case 2
Input:
23456
99999
Output:
B
Case 3
Input:
AAAAA
KKKKK
Output:
A
Case 4
Input:
2222
3333
Output:
UNKNOWN
Case 5
Input:
7
8
Output:
UNKNOWN
About This Question
This is a candidate experience report from a rippling interview for a swe role during the coding round.
It covers the following topics: Strings .
Topics
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About Rippling Interview Reports
This question was reported by a candidate who interviewed at Rippling. 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 Rippling are the higher-signal extractions to take from this report.
For broader preparation context, the Rippling 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 Rippling reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Rippling 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 Rippling 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.