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Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Google SWE interview focused on string processing problems, specifically the kind that push you toward recursion or DP. Pretty standard for this level but the depth expected on state definition and complexity analysis was no joke.

Questions Asked (1)

Q1

Solve a string processing problem using recursion or dynamic programming. Examples include edit distance, longest common subsequence, regex or wildcard matching, word break, or palindrome partitioning. You should be able to define the DP state, write the recurrence relation, handle base cases, and explain your memoization or tabulation approach, plus give time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is the kind of question that feels manageable until they ask you to formally define the state.

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AI HintsAI Generated

Suggested Approach

Clarify the problem, then define the DP state and recurrence relation before coding. Discuss both memoization and tabulation, and analyze time and space complexity. Walk through a small example to validate your approach.

Pro tip: Start with a brute-force recursive solution, then optimize with memoization. This demonstrates systematic problem-solving and makes the DP transition natural.

1. Clarify and Define

Restate the problem in your own words, ask clarifying questions, and define the DP state clearly (e.g., dp[i][j] represents the minimum edit distance between first i characters of word1 and first j characters of word2).

2. Derive Recurrence

Write the recurrence relation based on the last characters or decisions. For example, if characters match, dp[i][j] = dp[i-1][j-1]; else, take min of insert, delete, replace plus 1.

3. Handle Base Cases

Define base cases such as dp[0][j] = j (all insertions) and dp[i][0] = i (all deletions). Explain why these are correct.

4. Choose Implementation

Decide between memoization (top-down) and tabulation (bottom-up). Discuss trade-offs: memoization is easier to write from recurrence, tabulation avoids recursion overhead and allows space optimization.

5. Analyze Complexity

State time and space complexity. For edit distance, O(m*n) time and O(m*n) space, with possible O(min(m,n)) space optimization. Explain how you derived it.

Key Points to Mention

  • Definition of DP state and what it represents
  • Recurrence relation with clear cases
  • Base cases and initialization
  • Memoization vs tabulation trade-offs
  • Time and space complexity analysis
  • Space optimization techniques (e.g., using 1D array)

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