rippling·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Rippling SWE interview that built on a previous poker hand problem, this time adding incomplete data into the mix. The twist made it way more interesting than I expected, and the 'better approach' they were fishing for took me a minute to fully articulate.

Questions Asked (1)

You're extending a poker hand comparison function to handle incomplete hands where some cards may be missing due to network issues. Given two partial hands, implement a function that returns 'win', 'loss', 'tie', or 'unknown'. The optimal approach: compute best-case and worst-case hand strength for each player, and only return a definitive result if one player's worst case strictly beats the other's best case.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was the naive version: only return a result when both hands are complete, otherwise just say 'unknown'.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define what 'partial hand' means (missing cards, unknown suits/ranks) and the hand ranking rules. Then propose a solution that computes the best and worst possible hand strengths for each player by considering all possible completions of the missing cards, and compares them to return a definitive result only when one player's worst case beats the other's best case. Finally, discuss the time complexity and potential optimizations, such as pruning or memoization, to handle the combinatorial explosion.

Pro tip: Emphasize that the 'unknown' result is not a failure but a correct and safe outcome when information is incomplete; this shows you prioritize correctness over forcing a decision. Also, mention that you would validate the approach with edge cases like multiple missing cards or ambiguous hand rankings.

1. Clarify requirements and assumptions

Ask questions to confirm the rules: how many cards are missing, are they completely unknown or partially known (e.g., known rank but unknown suit), and what hand ranking system is used. Confirm that 'unknown' is acceptable when the outcome is not guaranteed.

2. Define best-case and worst-case hand strength

For each player, determine the strongest and weakest possible 5-card hand given the known cards and the set of missing cards. This involves enumerating all possible completions from the remaining deck.

3. Compare extremes to determine outcome

If player A's worst-case hand beats player B's best-case hand, return 'win' for A (or 'loss' for B). If B's worst beats A's best, return 'loss' for A. If neither dominates, return 'unknown'.

4. Analyze complexity and optimize

Discuss the combinatorial explosion: with k missing cards, there are C(remaining, k) possibilities. Suggest optimizations like pruning, dynamic programming, or precomputed hand rankings to make it feasible.

5. Test with edge cases

Mention testing scenarios: no missing cards (should behave like normal comparison), all cards missing (always 'unknown'), and cases where one player's hand is fully known while the other's is partial.

Key Points to Mention

Hand ranking rules and how to compare two 5-card hands (e.g., standard poker rankings).
Enumeration of all possible completions for missing cards, considering the remaining deck.
Best-case and worst-case hand strength computation for each player.
The dominance condition: definitive result only if one player's worst > other's best.
Time complexity: O(C(n, k)) where n is remaining cards and k is missing cards; potential optimizations.
Edge cases: no missing cards, all cards missing, and partial information (e.g., known rank but unknown suit).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.