moveworks·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Moveworks software engineering interview with a coding question that was more involved than it first looked. The problem was framed around Hangman but was really testing how you think about frequency analysis and candidate filtering under constraints.

Questions Asked (1)

Given a Hangman game state, implement a function that picks the next best letter to guess. You get the current pattern with revealed letters and blanks, the set of already-guessed letters, and a word list. Filter the word list to candidates that match the pattern and don't contain any letters known to be wrong, then pick the unguessed letter with the highest total frequency across those candidates. Walk through your input preprocessing, algorithm design, time and space complexity, and how you'd break ties deterministically.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I spent the first few minutes just parsing what they were actually asking.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then outline a step-by-step algorithm: preprocess the word list into a frequency map or trie, filter candidates based on the pattern and guessed letters, compute letter frequencies across candidates, and select the best letter with deterministic tie-breaking. Finally, analyze time and space complexity and discuss potential optimizations.

Pro tip: Demonstrate awareness of real-world trade-offs by discussing how the algorithm scales with large word lists and suggesting optimizations like indexing or early pruning, and always mention deterministic tie-breaking to ensure reproducibility.

1. Clarify requirements and edge cases

Ask about input format, word list size, pattern representation, and constraints (e.g., case sensitivity, non-alphabetic characters). Identify edge cases like no candidates, all letters guessed, or pattern with no blanks.

2. Preprocess inputs

Normalize the word list (e.g., lowercase), build a data structure for efficient filtering (e.g., index by length or letter positions), and parse the pattern and guessed letters into sets for quick lookup.

3. Filter candidate words

Iterate through the word list and keep words that match the pattern (same length, revealed letters in correct positions) and contain none of the incorrectly guessed letters. Use the preprocessed index to reduce the search space.

4. Compute letter frequencies and select best guess

For each unguessed letter, count its total occurrences across all candidate words. Choose the letter with the highest frequency. For ties, use a deterministic rule (e.g., alphabetical order or frequency in the original word list).

5. Analyze complexity and discuss optimizations

State time complexity: O(N * L) for filtering plus O(C * L) for frequency counting, where N is word list size, L is word length, and C is candidate count. Space complexity: O(N * L) for storage. Suggest optimizations like precomputed frequency maps or trie-based filtering.

Key Points to Mention

Pattern matching with wildcards and position-specific constraints
Efficient filtering using precomputed indices or tries to avoid scanning the entire word list
Frequency counting across candidates and selecting the letter with maximum total occurrences
Deterministic tie-breaking (e.g., alphabetical order) for reproducibility
Time and space complexity analysis with respect to word list size and word length
Handling edge cases such as no candidates, all letters guessed, or invalid inputs

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