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

Intermediate
Jun 2026

Summary

Amazon SWE online assessment with a greedy/simulation problem about inventory price adjustments. Pretty straightforward setup but the operation mechanics took me a minute to fully parse.

Questions Asked (1)

Q1

Given an array of product prices and two integers k (max adjustment per operation) and d (target max price difference), find the minimum number of operations to make the difference between the highest and lowest prices strictly less than d. Each operation lets you increase one price and decrease another by the same amount p, where p is at most k.

Algorithms & Data Structures
Author's notes

The operation definition tripped me up at first because x and y can be the same index, which felt weird.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and constraints, then propose a greedy strategy that repeatedly reduces the maximum and increases the minimum by the maximum allowed amount k, counting operations until the difference is less than d. Analyze the time complexity and discuss potential optimizations using sorting and two pointers.

Pro tip: Mention that the greedy approach is optimal because each operation maximally reduces the range, and highlight that the problem can be solved in O(n log n) time by sorting and using two pointers, which is efficient for large inputs.

1. Clarify the problem

Restate the problem in your own words and ask clarifying questions about edge cases, such as what happens if the difference is already less than d, or if it's impossible to achieve.

2. Identify the greedy strategy

Explain that in each operation, to reduce the range as much as possible, you should decrease the current maximum by k and increase the current minimum by k (or as much as needed to reach the target).

3. Simulate efficiently

Describe how to simulate the process efficiently: sort the array, use two pointers to track the current min and max, and update them after each operation, counting operations until the difference is less than d.

4. Analyze complexity

State that sorting takes O(n log n) and the simulation takes O(n) operations in the worst case, leading to O(n log n) overall time and O(1) extra space if sorting in place.

5. Discuss edge cases and alternatives

Mention edge cases like all elements equal, d <= 0, or k = 0, and briefly discuss if a binary search on the answer could be used, though greedy is simpler.

Key Points to Mention

  • Greedy choice: always adjust the current min and max by the maximum allowed amount k.
  • Optimality: each operation reduces the range by at most 2k, so greedy is optimal.
  • Efficiency: sort the array and use two pointers to avoid scanning the entire array each time.
  • Time complexity: O(n log n) due to sorting, with O(n) simulation steps.
  • Edge cases: handle when the initial difference is already less than d, or when k=0 and difference >= d.
  • Correctness proof: argue that any other strategy cannot use fewer operations because it would reduce the range by less.

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