Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Google SWE interview focused on a word prediction data structure problem. Pretty design-heavy for a coding round, which I wasn't expecting.

Questions Asked (1)

Design a word predictor backed by a map of maps (word to a map of next-word counts). Implement a record(prev, next) method that increments the successor count, and a predict(prev) method that returns the most frequent next word. Then discuss time and space complexity, and how you'd extend this to n-grams.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I got the basic structure down pretty quickly, a map where each key points to another map of candidates and their frequencies.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then design the data structure as a nested hash map (word -> {next_word: count}). Implement record and predict methods, ensuring predict efficiently finds the max count. Finally, analyze time/space complexity and discuss extensions to n-grams, including trade-offs and optimizations.

Pro tip: Mention that predict can be optimized by maintaining a max-heap or caching the top prediction per word, but note the trade-off with update complexity. Also, discuss handling ties and unseen words gracefully.

1. Clarify requirements and constraints

Ask about input size, expected frequency of record vs predict, memory limits, and whether ties need deterministic resolution. This shows you consider real-world usage.

2. Design the data structure

Propose a hash map where each key is a word and the value is another hash map mapping next words to their counts. Discuss why this gives O(1) average record time.

3. Implement record and predict

For record, increment the count in the inner map. For predict, iterate over the inner map to find the word with the maximum count, handling ties (e.g., return any or the lexicographically smallest).

4. Analyze complexity

Record: O(1) average time, O(1) space per new pair. Predict: O(k) time where k is the number of distinct next words for prev, O(1) extra space. Overall space: O(total distinct pairs).

5. Extend to n-grams

Generalize by using a tuple of n-1 words as the key, mapping to next-word counts. Discuss increased space and potential sparsity, and mention smoothing or backoff techniques.

Key Points to Mention

Use of nested hash maps for efficient O(1) average-time record operations.
Predict method's linear scan over successors; possible optimization with a max-heap or cached top prediction.
Handling ties and unseen previous words (e.g., return null or a default).
Time complexity: record O(1), predict O(k) where k = distinct successors.
Space complexity: O(V + E) where V is vocabulary size and E is number of distinct word pairs.
N-gram extension: key becomes a tuple of n-1 words, leading to exponential growth in state space; discuss pruning, backoff, or smoothing.

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