Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Google phone screen, algorithms focused. One question the whole time, but it had layers and they kept pushing until I got to the frequency-based optimization. Not a bad experience, just intense for what felt like a simple premise.

Questions Asked (1)

Design an algorithm to guess an unknown 5-letter word using only a function that checks whether a specific letter appears at a specific position. Start with brute force, then optimize using letter and position frequency data from a dictionary.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with the obvious: just iterate through all 26 letters for each of the 5 positions, call check() each time, done.

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AI HintsAI Generated

Suggested Approach

Start by describing a brute-force approach that systematically checks each position with all 26 letters, then optimize by leveraging letter and position frequency data from a dictionary to prioritize likely candidates. Emphasize the trade-offs between worst-case guarantees and average-case efficiency, and discuss how to adapt the strategy based on feedback.

Pro tip: Demonstrate awareness of real-world constraints: the checker function may be expensive, so minimizing calls is crucial. Also, mention that the dictionary may not contain the target word, so the algorithm should gracefully handle that case.

1. Clarify the problem and constraints

Ask clarifying questions: Is the dictionary known? Can we preprocess it? How many queries can we make? What is the cost of each query? This ensures you understand the problem scope.

2. Brute-force baseline

Propose a simple algorithm: for each position (1 to 5), try all 26 letters until a match is found. This guarantees finding the word in at most 5*26 = 130 queries, but is inefficient.

3. Optimize with frequency analysis

Preprocess the dictionary to compute letter frequencies per position. Then, for each position, query letters in descending order of frequency. This reduces the expected number of queries significantly.

4. Further optimization and trade-offs

Consider using a decision tree or information gain to choose queries that maximize expected information. Discuss trade-offs: precomputation time vs. query efficiency, and handling words not in the dictionary.

5. Analyze complexity and edge cases

Analyze worst-case and average-case query counts. Discuss edge cases: repeated letters, words with rare letters, and the possibility that the word is not in the dictionary.

Key Points to Mention

Brute-force approach: 5 positions × 26 letters = 130 queries worst-case.
Preprocessing dictionary to compute positional letter frequencies.
Querying letters in order of decreasing frequency to minimize expected queries.
Trade-offs: precomputation cost vs. query cost, and worst-case vs. average-case performance.
Handling words not in the dictionary: fallback to brute-force or report failure.
Potential use of information theory (e.g., entropy) to select optimal queries.

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