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

Intermediate
Apr 2026

Summary

Amazon SWE coding question involving merging two commit sequences while minimizing inversion-based conflicts. Feels like a DP problem dressed up in domain language.

Questions Asked (1)

Q1

Given two strings representing commit sequences from two branches, merge them into a single interleaved sequence that preserves the relative order of each branch. A conflict is defined as an inversion pair where a lower-priority character appears before a higher-priority one. Return the minimum number of conflicts across all valid merges.

Algorithms & Data Structures
Author's notes

Took me a while to even parse what a 'conflict' meant here.

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AI HintsAI Generated

Suggested Approach

Model the problem as finding a minimum-cost interleaving of two sequences where the cost is the number of inversions between elements of different branches. Use dynamic programming with state (i, j) representing the number of elements taken from each branch, and compute the minimum conflicts by considering the last taken element and counting inversions with the remaining elements. Optimize the transition by precomputing prefix counts to achieve O(n*m) time.

Pro tip: Clarify the definition of 'priority' and 'conflict' upfront—interviewers often expect you to ask about tie-breaking or whether conflicts are counted globally or locally. Also, mention that the DP can be optimized to O(n*m) space using rolling arrays, showing awareness of memory constraints.

1. Clarify the problem and define terms

Ask clarifying questions to confirm what 'priority' means (e.g., character order in alphabet or given priority list) and how conflicts are counted (inversions between elements from different branches). Ensure you understand that the merge must preserve the relative order of each branch.

2. Identify the optimal substructure

Recognize that the minimum conflicts for merging prefixes of lengths i and j depends only on the minimum conflicts for smaller prefixes. Define DP[i][j] as the minimum conflicts to merge the first i elements of branch A and first j elements of branch B.

3. Formulate the DP recurrence

For each state (i, j), consider taking the next element from A or B. If taking from A, add the number of conflicts created with the remaining elements of B (i.e., count of elements in B[j:] that have lower priority than A[i]). Similarly for taking from B. Take the minimum of the two options.

4. Optimize with precomputation

Precompute for each position in A and B the number of conflicts that would be added if that element is taken next. This can be done using prefix sums or frequency arrays to avoid O(n) per transition, reducing overall time to O(n*m).

5. Analyze complexity and edge cases

State that time complexity is O(n*m) and space can be O(min(n,m)) with rolling arrays. Discuss edge cases: empty strings, all elements from one branch, and when priorities are equal (no conflicts).

Key Points to Mention

  • Dynamic programming state definition: DP[i][j] = min conflicts for prefixes of length i and j.
  • Transition: min(DP[i-1][j] + conflicts from taking A[i-1], DP[i][j-1] + conflicts from taking B[j-1]).
  • Conflict counting: number of inversions between elements of different branches, based on priority order.
  • Precomputation of conflict counts using prefix sums or frequency arrays for O(1) transition.
  • Time complexity O(n*m) and space optimization to O(min(n,m)).
  • Edge cases: empty strings, equal priorities, and all elements from one branch.

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