Start by clarifying the problem constraints (e.g., array size, duplicates, negative numbers) and then propose an efficient solution using a hash map to store complements. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss potential edge cases.
Pro tip: At Amazon, emphasize scalability and real-world application: mention that the hash map approach is O(n) and can handle large datasets, aligning with Amazon's customer obsession and operational excellence. Also, proactively discuss trade-offs and alternative approaches to show depth.
Ask about input constraints: array size, possible values, duplicates, and whether the array is sorted. Confirm that exactly one solution exists and that you cannot use the same element twice.
Select a hash map (dictionary) to store each number's complement and its index. This allows O(1) lookups and a single pass through the array.
Iterate through the array, for each element check if its complement (target - current) exists in the hash map. If yes, return the stored index and current index. Otherwise, store the current element and its index.
State that time complexity is O(n) because we traverse the array once, and space complexity is O(n) for the hash map. Compare with brute force O(n^2) to highlight efficiency.
Discuss edge cases: negative numbers, zero, duplicates, and large arrays. Suggest testing with a few examples to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a sliding window with two pointers to maintain a window of unique characters, expanding the right pointer and shrinking the left when a duplicate is found. Track the maximum window length seen. This yields O(n) time and O(min(n, alphabet)) space.
Pro tip: At Amazon, emphasize scalability and edge cases: discuss how your solution handles large inputs and Unicode characters, and mention that you'd test with empty strings and all unique characters.
Ask about character set (ASCII vs Unicode), case sensitivity, and expected input size to determine constraints.
Explain that you'll use two pointers (left and right) to represent a window of unique characters, and a hash map to store the last seen index of each character.
Iterate the right pointer through the string; if the current character is in the map and its index is >= left, move left to that index + 1. Update the map with the current index and compute the window length.
State that time complexity is O(n) since each character is visited at most twice, and space complexity is O(min(n, m)) where m is the alphabet size.
Run through edge cases like empty string, all unique characters, and strings with repeating patterns to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.