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

Intermediate

May 2026

Summary

Amazon OA for a SWE role, one algorithmic problem involving array manipulation with some tricky greedy logic. Pretty standard OA format but the problem had enough edge cases to trip you up if you weren't careful.

Questions Asked (1)

Given an array of package weights and an integer k, build a resulting array by either discarding the first element or selecting it and removing the next k elements after it. The resulting array must be non-increasing and lexicographically maximal. Return that array.

Algorithms & Data Structures

Author's notes

The non-increasing constraint threw me off at first because I kept conflating it with the selection strategy itself.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then design a greedy algorithm that processes the array from left to right, maintaining a monotonic stack to ensure the result is non-increasing and lexicographically maximal. Validate the approach with examples and analyze time/space complexity.

Pro tip: Emphasize that the greedy choice is optimal because selecting a larger element earlier always yields a lexicographically larger array, and skipping k elements is a fixed cost. Also, mention that using a stack allows efficient backtracking to maintain the non-increasing property.

1. Understand the problem and constraints

Restate the problem in your own words, ask clarifying questions about input size, element ranges, and whether k can be larger than the array length. Identify edge cases like empty array, k=0, or k >= n.

2. Design a greedy strategy

Decide to process elements from left to right, always trying to pick the largest possible element that allows a valid non-increasing sequence. Use a stack to maintain the chosen elements and enforce the non-increasing property by popping smaller elements when a larger one is encountered, while respecting the skip constraint.

3. Implement the algorithm

Iterate through the array, and for each element, while the stack is not empty and the top is less than the current element and we can still skip the required number of elements (tracked by a counter), pop the stack. Then push the current element and increment the skip counter. Finally, truncate the stack to the desired length if needed.

4. Test with examples and edge cases

Walk through small examples, including cases where k=0, k=1, and k is large. Verify that the result is non-increasing and lexicographically maximal. Check edge cases like all elements equal or strictly increasing/decreasing.

5. Analyze complexity and optimize

State that the algorithm runs in O(n) time and O(n) space, as each element is pushed and popped at most once. Discuss potential optimizations or alternative approaches if needed.

Key Points to Mention

Greedy choice property: selecting the largest possible element at each step leads to a lexicographically maximal result.
Monotonic stack to maintain non-increasing order and efficiently remove smaller elements.
Skip counter to ensure exactly k elements are removed after each selected element.
Edge cases: empty array, k=0, k >= n, all elements equal.
Time and space complexity: O(n) time, O(n) space.
Proof of correctness: exchange argument showing that any deviation from the greedy choice yields a lexicographically smaller or invalid result.

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