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Salesforce·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Salesforce coding interview with a classic DP problem that sounds straightforward until you realize the substring vs subsequence distinction actually matters a lot for how you set up your recurrence.

Questions Asked (1)

Q1

Given two strings, find the length of the longest string that is both a subsequence of the first string and a contiguous substring of the second.

Algorithms & Data Structures
Author's notes

The subsequence vs substring split is what makes this tricky.

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AI HintsAI Generated

Suggested Approach

Clarify the problem by restating it and discussing examples. Then, propose an efficient algorithm, such as dynamic programming or sliding window, and analyze its time and space complexity. Finally, walk through a small example to validate the approach.

Pro tip: Mention that you would first check for edge cases like empty strings or no common characters, and discuss trade-offs between different approaches (e.g., DP vs. binary search with hashing) to show depth.

1. Understand and Clarify

Restate the problem in your own words and ask clarifying questions about constraints, input sizes, and expected output. Confirm with examples.

2. Brainstorm Approaches

Discuss possible strategies: brute force, dynamic programming, or using a sliding window over the second string while checking subsequence in the first. Consider time and space complexity.

3. Select and Explain Algorithm

Choose the most efficient approach and explain it step-by-step. For example, use DP where dp[i][j] represents the length of the longest common subsequence ending at i in first string and j in second, but only consider contiguous substrings in the second.

4. Analyze Complexity

State the time and space complexity of your solution. Discuss if any optimizations are possible.

5. Test with Example

Walk through a small example to demonstrate correctness and edge cases.

Key Points to Mention

  • Definition of subsequence vs. substring
  • Dynamic programming state and transition
  • Time and space complexity analysis
  • Edge cases: empty strings, no common characters, repeated characters
  • Optimization techniques like binary search with hashing or sliding window
  • Trade-offs between different approaches

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