Start by clarifying the problem constraints and tie-breaking rules, then propose a solution using a hash map to count frequencies and a sort with a custom comparator. Discuss time and space complexity, and consider edge cases like empty input or all unique elements.
Pro tip: At Amazon, interviewers value candidates who proactively discuss trade-offs and scalability. Mention that for large datasets, a bucket sort approach can achieve O(n) time, and always confirm tie-breaking behavior with the interviewer.
Ask about input size, data types, and how ties should be broken (e.g., by original order, lexicographically, or any order). Confirm if the output should be a list of elements or pairs.
Use a hash map to count frequencies in O(n) time. For sorting, consider using a list of (element, frequency) pairs and sort with a custom comparator.
Define a deterministic tie-breaking rule, such as sorting tied elements by their natural order (for strings, lexicographically; for integers, numerically) or by first occurrence.
State that the hash map approach takes O(n) time and O(n) space, and sorting takes O(k log k) where k is the number of distinct elements. Mention that bucket sort can achieve O(n) if frequencies are bounded.
Walk through examples like empty input, single element, all elements same, and ties. Verify that the tie-breaking rule is applied consistently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.