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

Intermediate

Jul 2026

Summary

Amazon SWE online assessment with a sliding window / greedy problem that looks approachable until you actually try to pin down the cost accounting. No behavioral, just the one algorithmic problem, but it had enough edge cases to keep me busy.

Questions Asked (1)

Given a binary array and a fixed window size k, you can repeatedly pick any window of exactly length k, pay the cost equal to the sum of that window, then zero out exactly one 1 inside it. What is the minimum total cost to eliminate all 1s?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was pure greedy: always pick the window with the fewest 1s so the cost per removal is minimized.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and edge cases, then propose a greedy strategy that always picks the window with the smallest sum containing at least one 1, and zero out a 1 in it. Analyze the time complexity and discuss potential optimizations using a priority queue or segment tree.

Pro tip: Emphasize that the greedy choice is optimal because zeroing a 1 in a window with minimal sum minimizes immediate cost and cannot increase future costs. Also, mention that if multiple 1s are in the window, zeroing any one is equivalent for future steps.

1. Clarify the problem

Restate the problem in your own words, confirm the goal is to minimize total cost, and ask about constraints (e.g., array size, k, number of 1s).

2. Propose a greedy approach

Suggest repeatedly selecting the window of length k with the smallest sum that contains at least one 1, and zeroing out one 1 in that window.

3. Justify optimality

Explain why the greedy choice is optimal: choosing a window with minimal sum minimizes the immediate cost, and zeroing a 1 cannot increase the sum of any window, so future costs are not adversely affected.

4. Analyze complexity and optimizations

Discuss naive O(n^2) time and how to optimize using a priority queue or segment tree to find the minimal-sum window containing a 1 efficiently.

5. Handle edge cases

Consider cases like no 1s, k larger than array, or windows with no 1s; ensure the algorithm handles them correctly.

Key Points to Mention

Greedy choice property: always pick the window with the smallest sum containing a 1.
Optimal substructure: zeroing a 1 reduces the problem size without increasing future costs.
Data structures for efficiency: priority queue or segment tree to find minimal-sum window.
Time complexity: naive O(n^2) vs optimized O(n log n) or O(n) with sliding window.
Edge cases: no 1s, k > n, windows without 1s.
Trade-offs: simplicity vs performance, and potential for a more complex optimal algorithm.

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