Amazon·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate

May 2026

Summary

Amazon SWE online assessment, just one coding problem about finding the top K elements from an array. Pretty standard stuff but the O(n log k) constraint is the whole point of the exercise.

Questions Asked (1)

Given an integer array of length n and an integer k, return the k largest elements sorted in descending order. Target time complexity is O(n log k).

Algorithms & Data Structures

Author's notes

The naive sort-the-whole-array approach gets you the right answer but misses the point entirely.

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AI HintsAI Generated

Suggested Approach

Use a min-heap of size k to efficiently track the k largest elements in O(n log k) time. Iterate through the array, maintaining the heap so that it always contains the k largest elements seen so far. Finally, extract the elements from the heap and sort them in descending order.

Pro tip: Mention that for small k relative to n, the heap approach is optimal, but if k is close to n, sorting the entire array might be simpler and still efficient. Also, clarify that the output should be sorted descending, which requires an extra O(k log k) step.

1. Clarify requirements and edge cases

Confirm the expected output format (sorted descending) and discuss edge cases like k=0, k>=n, or duplicate elements.

2. Choose the right data structure

Select a min-heap of size k to maintain the k largest elements, ensuring O(n log k) time complexity.

3. Iterate and maintain the heap

For each element, if the heap has fewer than k elements, add it; otherwise, if the element is larger than the heap's minimum, replace the minimum.

4. Extract and sort the result

After processing all elements, extract the k elements from the heap and sort them in descending order to produce the final output.

5. Analyze complexity and discuss alternatives

Explain that the time complexity is O(n log k) and space is O(k). Mention alternative approaches like quickselect for average O(n) time, but note that it doesn't guarantee sorted output.

Key Points to Mention

Min-heap of size k to keep track of the k largest elements
Time complexity: O(n log k) due to heap operations
Space complexity: O(k) for the heap
Final sorting step: O(k log k) to return elements in descending order
Edge cases: k=0, k>=n, empty array, duplicates
Alternative approaches: sorting entire array O(n log n), quickselect O(n) average but not sorted

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.