Google·Software Engineer·Technical Phone Screen·Intermediate

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Jun 2026

Summary

Google SWE interview with an interactive coding problem. The problem was a word-guessing game where you had to find a hidden word within 10 guesses using positional match feedback, which sounds straightforward but has some real design complexity underneath.

Questions Asked (1)

Design a strategy to find a hidden word from a candidate list within 10 guesses. After each guess, you receive the number of positions where your guess matches the secret word exactly. How do you approach this?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The interactive part tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints: word length, candidate list size, and whether guesses must be from the list. Then propose an information-theoretic strategy: use a scoring function to evaluate each candidate guess by how evenly it partitions the remaining possibilities based on the match count, and iteratively narrow down the list. Discuss trade-offs between optimal worst-case performance and computational feasibility, and mention practical optimizations like precomputation or heuristics.

Pro tip: Emphasize that the optimal strategy depends on the candidate list size and word length; for small lists, exhaustive search is fine, but for large lists, you need a greedy heuristic like minimizing the expected remaining candidates. Also, mention that the first guess should maximize information gain, often by choosing a word with diverse letters, not necessarily from the list.

1. Clarify Constraints and Assumptions

Ask about word length, size of candidate list, whether guesses must be from the list, and if the secret word is guaranteed to be in the list. This ensures you design the right strategy.

2. Model the Problem as Information Gain

Treat each guess as a query that partitions the candidate set based on the number of exact matches. The goal is to reduce the candidate set to one within 10 guesses.

3. Design a Scoring Function for Guesses

For each possible guess, compute the distribution of match counts over the remaining candidates. Choose the guess that minimizes the maximum or expected remaining candidates (e.g., entropy or worst-case size).

4. Iterate and Update

After each guess, filter the candidate list to those consistent with the received match count. Repeat until one candidate remains or guesses are exhausted.

5. Analyze Complexity and Trade-offs

Discuss time/space complexity of the strategy, and trade-offs between optimality and efficiency. Mention potential optimizations like precomputing partitions or using heuristics for large lists.

Key Points to Mention

Information theory: each guess should maximize information gain (e.g., minimize entropy or expected remaining candidates).
Worst-case vs. average-case performance: consider both, but for a 10-guess limit, worst-case guarantees are crucial.
Candidate list size and word length: these determine whether exhaustive search is feasible or if heuristics are needed.
First guess strategy: often choose a word that is not necessarily in the list but has diverse letters to maximize partitioning.
Data structures: use hash maps or tries to efficiently filter candidates based on match counts.
Trade-offs: optimal strategy may be computationally expensive; discuss approximations like greedy selection or beam search.

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