Jumped straight to the hash set approach, O(n) time and space, insert each element and bail early on a collision.
Start by clarifying the problem constraints (e.g., input size, memory limits, whether the array can be modified). Then propose a solution using a hash set to track seen strings, which offers O(n) time and O(n) space. If needed, discuss alternative approaches like sorting (O(n log n) time, O(1) extra space) and their trade-offs.
Pro tip: Netflix values pragmatic engineering, so emphasize the trade-offs between time and space complexity and how you'd choose based on real-world constraints like memory availability or the need for speed.
Ask about input size, memory limits, whether the array can be modified, and if there are any time/space constraints. This shows you think before coding.
Explain that you can iterate through the array, adding each string to a hash set. If a string is already in the set, return true; otherwise, return false after the loop.
State that the hash set approach runs in O(n) average time and uses O(n) space. Mention that sorting could reduce space to O(1) but increases time to O(n log n).
Discuss empty arrays, single-element arrays, and large inputs that might not fit in memory. Mention that for very large data, a distributed approach or external sorting might be needed.
Write clean code with meaningful variable names, and walk through a few test cases (e.g., ["a","b","a"] returns true, ["a","b","c"] returns false).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.