Start by clarifying the problem constraints (e.g., array size, whether the array is sorted, if there are multiple valid pairs, and if the same element can be used twice). Then, propose an efficient solution using a hash map to store seen elements and their indices, achieving O(n) time and O(n) space. If the array is sorted, mention the two-pointer approach as an alternative with O(n) time and O(1) space.
Pro tip: At Amazon, emphasize scalability and real-world application: discuss how this problem relates to feature matching or recommendation systems, and mention handling edge cases like no solution or duplicate values. Also, briefly analyze time and space complexity to show you consider efficiency.
Ask about input size, whether the array is sorted, if there are duplicate elements, and if exactly one solution exists. Confirm the expected return format (e.g., indices in any order).
Mention the O(n^2) brute-force method, then propose the hash map approach for O(n) time. If the array is sorted, mention the two-pointer technique for O(n) time and O(1) space.
Iterate through the array, for each element check if target - element exists in the hash map. If yes, return the stored index and current index; otherwise, store the element and its index.
State time and space complexity (O(n) time, O(n) space). Discuss edge cases: no solution, multiple solutions, negative numbers, and duplicate values.
Connect the problem to ML scenarios, such as finding pairs of features with a target correlation or matching user-item interactions. Highlight scalability and optimization.
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