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Google·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say
May 2026

Summary

Google SWE coding round with a tricky Morse code decoding problem. The core challenge was handling ambiguous splits with no delimiters, plus outputting both a count (mod 1e9+7) and the lexicographically smallest K decoded strings.

Questions Asked (1)

Q1

Given a continuous Morse code string with no delimiters between letters and a custom mapping of up to 26 letters to their codes, write a program that outputs the total number of valid decodings (mod 1,000,000,007) and the lexicographically smallest up to 10 decoded strings.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The counting part felt approachable once I framed it as a DP over positions in the string, basically dp[i] = number of ways to decode the first i characters.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a dynamic programming solution to count decodings and a modified DP with backtracking to find the lexicographically smallest strings. Discuss trade-offs between time and space, and how to handle the modulo and the top-10 limit.

Pro tip: Mention that you would use a trie or hash map for the custom Morse code mapping to allow efficient prefix checks, and emphasize the importance of modular arithmetic to avoid overflow.

1. Clarify Requirements and Edge Cases

Ask about input size, character set, and whether the mapping is fixed or can have variable-length codes. Discuss edge cases like empty string, no valid decodings, and codes that are prefixes of others.

2. Design DP for Counting Decodings

Define dp[i] as the number of ways to decode the prefix of length i. Transition by checking all possible code lengths ending at i, using the mapping. Apply modulo at each step.

3. Extend DP to Find Lexicographically Smallest Strings

Use DP to store the lexicographically smallest up to 10 strings for each prefix, or use backtracking with memoization to generate them in order. Ensure lexicographic order by sorting characters or codes appropriately.

4. Optimize and Analyze Complexity

Analyze time complexity O(n * L) where L is max code length, and space O(n * 10 * avg_length). Suggest optimizations like rolling arrays or pruning if needed.

5. Test with Examples and Discuss Trade-offs

Walk through a small example to verify correctness. Discuss trade-offs between storing all strings vs. generating on the fly, and how to handle large outputs.

Key Points to Mention

  • Dynamic programming state definition and transition
  • Handling modulo 1,000,000,007 to prevent overflow
  • Using a trie or hash map for efficient code lookup
  • Lexicographic ordering and how to generate smallest strings
  • Time and space complexity analysis
  • Edge cases: empty string, no valid decoding, overlapping codes

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