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Sumo Logic·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Got a coding question at Sumo Logic for a software engineer role, sliding window type problem. Pretty standard technical screen, nothing too surprising.

Questions Asked (1)

Q1

Given a string and an integer k, find the length of the longest substring where all characters are the same, given that you can replace at most k characters in the string.

Algorithms & Data Structures
Author's notes

Classic sliding window problem once you see it, but I spent a few minutes fumbling with a brute force approach before the pattern clicked.

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AI HintsAI Generated

Suggested Approach

Use a sliding window approach with a frequency map to track the count of each character in the current window. Expand the window by moving the right pointer, and when the number of replacements needed (window length minus max frequency) exceeds k, shrink the window from the left. Keep track of the maximum valid window length seen.

Pro tip: Clarify that the problem is equivalent to finding the longest substring where (window length - count of most frequent character) <= k. Mention that the sliding window is optimal because the condition is monotonic: if a window is invalid, any larger window containing it is also invalid.

1. Understand the problem and identify the condition

Restate the problem: find the longest substring that can be made uniform by replacing at most k characters. The key condition is that the number of characters to replace (window length - max frequency) must be <= k.

2. Choose the sliding window technique

Use two pointers (left and right) to represent a window. Expand the window by moving right, and maintain a frequency map of characters in the window.

3. Maintain and update the window

For each new character added, update its frequency and the max frequency seen so far. If (window length - max frequency) > k, shrink the window by moving left and decrementing the frequency of the character removed.

4. Track the maximum length

After each expansion (and possible shrink), update the maximum length of a valid window. The answer is the maximum length encountered.

5. Analyze complexity and edge cases

Time complexity is O(n) since each character is processed at most twice. Space complexity is O(1) for the frequency map (at most 26 lowercase letters). Handle edge cases like k >= string length or empty string.

Key Points to Mention

  • Sliding window technique with two pointers
  • Frequency map to track character counts in the window
  • Condition: window length - max frequency <= k
  • Time complexity O(n) and space complexity O(1) (or O(26))
  • Handling edge cases: empty string, k >= length, all characters same
  • Why the sliding window is optimal: monotonic property of the condition

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