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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google SWE coding round, one question about counting the number of ways to split a string. Pretty light on details but the problem itself had some meat to it.

Questions Asked (1)

Q1

Given a string, count the number of ways to split it into parts according to some defined criteria.

Algorithms & Data Structures
Author's notes

Classic combinatorics-meets-string-manipulation type problem.

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AI HintsAI Generated

Suggested Approach

First, clarify the splitting criteria and constraints, as the problem is underspecified. Then, propose a dynamic programming solution where dp[i] represents the number of ways to split the prefix ending at i, and transition by checking all valid previous split points. Optimize with precomputation or sliding window if the criteria allow.

Pro tip: Always discuss time and space complexity trade-offs and mention edge cases like empty string or no valid splits. This shows you think about robustness and efficiency, which is crucial at Google.

1. Clarify the problem

Ask questions to understand the splitting criteria, constraints, and expected output. For example, what defines a valid part? Are parts contiguous? Can they be empty?

2. Define the DP state

Let dp[i] be the number of ways to split the substring s[0..i-1] (or up to index i). Initialize dp[0] = 1 for the empty prefix.

3. Formulate the transition

For each i, iterate over possible previous split points j < i, and if the substring s[j..i-1] satisfies the criteria, add dp[j] to dp[i]. This yields O(n^2) time, which can be optimized.

4. Optimize if needed

If the criteria allow, use precomputation (e.g., prefix sums, hashing) or sliding window to reduce time complexity to O(n) or O(n log n). Discuss the trade-offs.

5. Analyze complexity and test

State the time and space complexity of your solution. Walk through examples, including edge cases like empty string, no valid splits, or all valid splits.

Key Points to Mention

  • Dynamic programming as the core technique
  • Time and space complexity analysis (O(n^2) vs optimized)
  • Handling edge cases (empty string, no valid splits)
  • Clarifying the splitting criteria and constraints
  • Potential optimizations like prefix sums or sliding window
  • Modular arithmetic if the count can be large

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