Start by clarifying the problem constraints (e.g., input size, memory limits) and then propose an efficient solution using a hash set to track seen episode IDs. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss potential optimizations or trade-offs.
Pro tip: Mention that this is essentially a cycle detection problem in a sequence, and you can optimize for space by using a bit array if episode IDs are within a known range. Also, relate it to Netflix's need for efficient duplicate detection in large-scale watch histories.
Ask about input size, memory constraints, and whether the list is sorted or if episode IDs have a known range. This helps determine the optimal approach.
Select a hash set for O(1) average-time lookups and insertions, or a bit array if the ID range is small and known. Discuss trade-offs between time and space.
Iterate through the list, checking if each episode ID is already in the set. If it is, return True; otherwise, add it to the set. After the loop, return False.
State that the time complexity is O(n) and space complexity is O(n) in the worst case. Mention that early termination can improve average performance.
Consider empty list, single element, and large datasets. Mention alternative approaches like sorting (O(n log n) time, O(1) space) or using a Bloom filter for approximate detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.