Seemed simple at first and I jumped straight to using a built-in count method, which worked but they pushed me to implement it manually.
Start by clarifying the problem: overlapping occurrences, case sensitivity, and expected input sizes. Then present a straightforward solution using a sliding window or built-in find, and discuss optimizations like KMP for large inputs. Finally, analyze time and space complexity and test with edge cases.
Pro tip: Mention that for large-scale text processing at LinkedIn, algorithms like KMP or Rabin-Karp are preferred over naive approaches due to their linear time complexity, and always consider overlapping matches unless specified otherwise.
Ask about overlapping occurrences, case sensitivity, and input constraints (e.g., string length, pattern length). This ensures you solve the correct problem.
Describe a naive approach: iterate through the string and check for the pattern at each position. Mention its O(n*m) time complexity.
Introduce KMP or Rabin-Karp for O(n+m) time, explaining how they avoid redundant comparisons. Discuss trade-offs.
State time and space complexity of your chosen solution. Cover edge cases: empty pattern, pattern longer than string, no matches, all matches, overlapping matches.
Walk through a small example, including overlapping cases, to demonstrate correctness. Optionally, mention unit testing.
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