← Amazon Interview Insights

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

Intermediate
Apr 2026

Summary

Amazon SWE online assessment with a math-heavy array problem. The kind of question where you need to really think about GCD properties before you even touch the code.

Questions Asked (1)

Q1

Given an integer array and a budget of k replacements, find the minimum possible 'weakness factor', defined as the length of the longest contiguous subarray whose GCD is greater than 1. You can replace any element with any value up to k times.

Algorithms & Data Structures
Author's notes

Took me a while to even parse what 'weakness factor' meant in this context.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the problem and constraints, then propose a binary search on the answer combined with a sliding window to check feasibility. For a given weakness factor W, determine if we can break all subarrays of length W+1 with GCD > 1 using at most k replacements, and binary search for the smallest W.

Pro tip: Mention that replacements can be chosen to break GCDs, and that the optimal strategy often involves setting elements to 1 or distinct primes; also note that the problem is equivalent to ensuring no window of length W+1 has all elements sharing a common prime factor.

1. Clarify and Restate

Confirm understanding of the problem: we can replace any element with any integer, up to k times, to minimize the longest contiguous subarray with GCD > 1. Ask about constraints (e.g., array size, value range) to guide algorithm choice.

2. Binary Search on Answer

The weakness factor is monotonic: if we can achieve weakness ≤ W, we can also achieve ≤ W+1. So binary search W from 0 to n, and for each mid, check if it's possible with ≤ k replacements.

3. Feasibility Check with Sliding Window

For a fixed W, we need to ensure no subarray of length W+1 has GCD > 1. Use a sliding window to find maximal segments where all elements share a common prime factor, and count the minimum replacements needed to break them.

4. Optimize Replacement Strategy

For each maximal segment of length L where all elements share a common prime, we need to replace at least floor(L/(W+1)) elements to break all windows of length W+1. Sum these over all segments and compare with k.

5. Complexity and Edge Cases

Analyze time complexity: binary search O(log n) times, each check O(n * number of primes) using prime factorization. Discuss edge cases: k=0, all elements same, array size 1, etc.

Key Points to Mention

  • Binary search on the answer (weakness factor) due to monotonicity.
  • Sliding window to find maximal segments with common prime factors.
  • Prime factorization to determine shared factors efficiently.
  • Greedy replacement: replace elements at positions that cover multiple windows (e.g., every W+1-th element in a segment).
  • Time complexity: O(n log n * log(max value)) or similar, and space complexity O(n).
  • Edge cases: k=0, k >= n, array with all 1s, etc.

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