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

Intermediate

May 2026

Summary

Amazon OA for a SWE role, got a dynamic programming style problem under the 'Segmentify' branding. The problem dressing was a bit much but the underlying puzzle was interesting once I stripped away the video frame flavor text.

Questions Asked (1)

Given a binary string of even length, find the minimum number of even-length subsegments (where each subsegment is all 0s or all 1s) such that the total number of character flips needed is minimized first, and then the subsegment count is minimized as a secondary goal.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The problem is dressed up as a video processing feature but it's really just a string partitioning problem.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem by restating it and confirming constraints. Then, propose an algorithm that minimizes flips first and subsegment count second, likely using dynamic programming or greedy with parity considerations. Finally, analyze time and space complexity and discuss potential optimizations.

Pro tip: Demonstrate awareness of the trade-off between minimizing flips and minimizing subsegments, and explain how you prioritize them. Mention that you would test edge cases like all same characters or alternating patterns.

1. Clarify the problem

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

2. Identify the primary objective

Focus on minimizing flips first. Consider that flips are needed to make subsegments uniform; determine the minimum flips required for any partition.

3. Optimize for secondary objective

Among all partitions achieving minimum flips, find the one with the fewest even-length subsegments. Use DP or greedy with parity tracking.

4. Analyze complexity

Derive time and space complexity of your approach. Discuss if it meets typical interview constraints (e.g., O(n) or O(n^2)).

5. Test with examples

Walk through small examples, including edge cases, to verify correctness and demonstrate understanding.

Key Points to Mention

Dynamic programming with state representing position and parity of current subsegment length.
Greedy approach: merge adjacent subsegments of same character to reduce count without increasing flips.
Parity constraint: each subsegment must have even length, so total length even ensures feasibility.
Trade-off between flips and subsegment count: sometimes more subsegments can reduce flips, but we prioritize flips.
Time complexity: aim for O(n) or O(n^2) depending on approach; space complexity O(n) for DP.
Edge cases: all characters same (0 flips, 1 subsegment if even length), alternating characters (many flips needed).

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