I went straight for a hashmap and it mostly worked, but I fumbled the duplicate counting part.
Start by clarifying the problem constraints (e.g., input size, whether the array is sorted, and if the output should be sorted). Then, propose an efficient solution using a hash set to track seen numbers, which allows O(n) time complexity while handling duplicates by storing unique pairs in a set. Finally, discuss trade-offs and potential optimizations, such as sorting the array first if needed.
Pro tip: At Amazon, interviewers value candidates who proactively discuss edge cases (e.g., empty array, no pairs, multiple duplicates) and the impact of data characteristics on algorithm choice. Mentioning how you'd handle large-scale data or streaming scenarios can set you apart.
Ask about input size, data types, whether the array is sorted, and if the output pairs need to be sorted or in any order. Confirm that each element can be used only once and that pairs should be unique.
Propose using a hash set to store seen numbers. For each number, check if its complement (target - number) exists in the set; if so, add the sorted pair to a result set to avoid duplicates. This yields O(n) time and O(n) space.
Explain how the set-based approach naturally handles duplicates by only adding unique pairs. Discuss edge cases like empty array, single element, no valid pairs, and multiple identical values.
State time and space complexity (O(n) time, O(n) space). Compare with alternative approaches like sorting + two-pointer (O(n log n) time, O(1) space) and explain when each is preferable.
Walk through a small example (e.g., [1,2,3,2,1], target=4) to demonstrate correctness. Summarize the solution and mention potential extensions (e.g., returning indices, handling large data).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.