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

Intermediate

Jun 2026

Summary

Amazon SWE online assessment with a simulation-style array problem. Nothing behavioral, just code. The problem looked manageable at first glance but the edge cases in the decrement logic had me second-guessing my implementation the whole way through.

Questions Asked (1)

Given an integer array and a number of operations n, repeatedly find the current min and max, record their sum, then decrement the max by 1. Return all n sums.

Algorithms & Data Structures

Author's notes

My first instinct was a sorted structure and i went with a max-heap plus tracking the global min separately.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient solution using a max-heap and a min-heap to track the current min and max. Explain how to update the heaps after decrementing the max, and analyze the time and space complexity.

Pro tip: Discuss the trade-offs between different approaches (e.g., sorting vs. heaps) and mention that the max-heap approach is optimal for large n, but be prepared to code a simpler solution if time is limited.

1. Clarify the problem

Ask clarifying questions about input constraints, edge cases (e.g., empty array, n=0, negative numbers), and whether the array can be modified in place.

2. Outline a naive approach

Describe a straightforward solution: for each operation, scan the array to find min and max, record sum, decrement max. Mention its O(n * m) time complexity.

3. Propose an optimized approach

Suggest using a max-heap to efficiently get the maximum and a min-heap for the minimum. Explain how to handle the decrement and reinsertion into both heaps.

4. Analyze complexity and edge cases

State the time complexity (O((m + n) log m) where m is array length) and space complexity (O(m)). Discuss edge cases like duplicate values and when the max becomes less than the min.

5. Code and test

Write clean code for the heap-based solution, then walk through a small example to verify correctness.

Key Points to Mention

Use of two heaps (min-heap and max-heap) to track min and max efficiently.
Handling duplicates and ensuring the heaps stay in sync after decrementing the max.
Time complexity analysis: O((m + n) log m) vs. O(n * m) for naive.
Space complexity: O(m) for the heaps.
Edge cases: empty array, n=0, all elements equal, negative numbers.
Potential optimization: if n is small, naive approach might be simpler and sufficient.

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