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Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jul 2026

Summary

Amazon SWE coding question, one problem, pretty algorithmic. The problem looked deceptively short but took me a while to fully wrap my head around what was actually being asked.

Questions Asked (1)

Q1

Given an unsorted array of size n and an integer k, consider all subsequences of size n-k. For each such subsequence, sort it and find the maximum difference between consecutive elements. Return the minimum of those maximum differences across all valid subsequences.

Algorithms & Data Structures
Author's notes

Took me an embarrassingly long time just to understand what they were asking.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and constraints, then propose a binary search on the answer combined with a greedy check. For a given maximum allowed difference D, determine if there exists a subsequence of size n-k where all consecutive sorted differences are ≤ D. This reduces to finding the longest subsequence with gaps ≤ D; if its length ≥ n-k, D is feasible.

Pro tip: Mention that the greedy check can be optimized using a sliding window or DP with binary search, and that the overall complexity is O(n log n log(max-min)). This shows you consider both correctness and efficiency.

1. Clarify the problem

Restate the problem in your own words and ask clarifying questions about constraints, edge cases, and expected input/output format.

2. Sort the array

Sorting simplifies the problem because any subsequence's sorted order is the same as its order in the sorted array. This allows us to work with indices in the sorted array.

3. Binary search on the answer

The answer lies between 0 and max-min. Use binary search to find the smallest D such that a valid subsequence exists with all consecutive differences ≤ D.

4. Feasibility check for a given D

For a fixed D, find the longest subsequence where consecutive elements differ by at most D. This can be done with a greedy two-pointer or DP with binary search, and check if its length ≥ n-k.

5. Return the minimum D

After binary search converges, return the smallest D that is feasible. Discuss time and space complexity.

Key Points to Mention

  • Sorting the array first to simplify subsequence selection.
  • Binary search on the answer space to efficiently find the minimum maximum difference.
  • Greedy or dynamic programming approach for the feasibility check.
  • Time complexity: O(n log n log(max-min)) with efficient check.
  • Edge cases: k=0 (subsequence is whole array), k=n-1 (subsequence size 1, answer 0), duplicates.
  • Proof of correctness: monotonicity of feasibility with respect to D.

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