moveworks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Interviewed for a Software Engineer role at Moveworks and got a Mastermind-style word guessing problem. The core challenge was designing a strategy to identify a secret word from a list using feedback from each guess, within a fixed number of attempts. Interesting problem but the guess-limit constraint is what makes it actually hard.

Questions Asked (1)

Given a list of candidate words of equal length, design a strategy to identify an unknown secret word by making guesses and receiving feedback on how many character positions match exactly. Your solution must find the word within a limited number of guesses.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The feedback mechanism is simple enough but figuring out how to prune the candidate list efficiently after each guess is where I got stuck for a bit.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., number of guesses allowed, feedback type) and then propose an information-theoretic approach: each guess should maximize the expected information gain by partitioning the candidate set as evenly as possible. Use a decision tree or minimax strategy to select guesses that minimize the worst-case number of remaining candidates, and prove that the secret word can be found within the limit.

Pro tip: Mention that in practice, you can precompute an optimal decision tree for small candidate sets, but for larger sets, a greedy entropy-based heuristic often works well. Also, discuss trade-offs between optimality and computational feasibility.

1. Clarify constraints and assumptions

Ask about the number of guesses allowed, the size of the candidate list, and whether the secret word is guaranteed to be in the list. Confirm that feedback is only the count of exact matches.

2. Model as a search problem

Represent the candidate set and feedback as a decision tree where each node is a guess and edges correspond to possible feedback values. The goal is to minimize the depth of the tree.

3. Choose a guess selection strategy

Use an information-theoretic approach: for each possible guess, compute the distribution of feedback over the remaining candidates and pick the guess that maximizes entropy (or minimizes the maximum partition size).

4. Analyze worst-case and average-case performance

Prove that the strategy finds the word within the limit by bounding the number of candidates that can remain after each guess. Discuss the trade-off between optimality and computational cost.

5. Discuss implementation and optimizations

Explain how to implement the strategy efficiently, e.g., using precomputed feedback tables or pruning. Mention that for small candidate sets, exhaustive search for the optimal decision tree is feasible.

Key Points to Mention

Information theory: entropy and expected information gain
Minimax strategy to minimize worst-case number of guesses
Decision tree representation and depth analysis
Trade-offs between optimality and computational complexity
Handling ties in feedback distribution (e.g., choose lexicographically first)
Proof of correctness and bound on number of guesses (e.g., log base (L+1) of N)

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