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

Intermediate
Jun 2026

Summary

Amazon SWE online assessment with a greedy algorithm problem. Pretty compact problem statement but the tricky part was figuring out the right greedy strategy to minimize cumulative overhead.

Questions Asked (1)

Q1

You're given a string containing '0', '1', and '?' characters. Replace every '?' with either '0' or '1' to minimize the total sum of overheads across all positions.

Algorithms & Data Structures
Author's notes

Greedy problem but I spent way too long second-guessing myself on what 'overhead at a position' actually meant before it clicked.

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AI HintsAI Generated

Suggested Approach

First, clarify the definition of 'overhead' and the exact cost function, as the problem statement is ambiguous. Then, propose a dynamic programming solution that processes the string left-to-right, maintaining the minimal cost for each possible last character. Finally, discuss time and space complexity and potential optimizations.

Pro tip: Always ask clarifying questions about ambiguous terms like 'overhead' before diving into a solution; it shows you prioritize correctness over speed. Also, mention that you'd test with edge cases like all '?' or alternating patterns.

1. Clarify the problem

Ask the interviewer to define 'overhead' and the cost function. For example, is it the number of adjacent differing bits, or something else? Confirm input constraints and expected output.

2. Define the cost function

Once clarified, formalize the cost. For instance, if overhead is the count of adjacent differing bits, then cost = sum over i of (s[i] != s[i-1]).

3. Design DP state and transition

Use DP where dp[i][c] = minimal cost for prefix up to i ending with character c. Transition: dp[i][c] = min over prev of dp[i-1][prev] + cost(prev, c), respecting fixed characters.

4. Implement and optimize

Implement the DP with O(n) time and O(1) space by keeping only the previous state. Handle '?' by trying both 0 and 1.

5. Test and discuss complexity

Walk through examples, including edge cases. State time and space complexity, and mention possible greedy alternatives if applicable.

Key Points to Mention

  • Dynamic programming approach with state representing the last character.
  • Time complexity O(n) and space complexity O(1) after optimization.
  • Handling of fixed characters versus '?' choices.
  • Edge cases: empty string, all '?', alternating patterns.
  • Potential greedy solution if cost function is simple (e.g., minimize transitions).
  • Clarification of ambiguous terms before solving.

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