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

Intermediate
May 2026

Summary

Amazon SWE online assessment with a string manipulation problem involving simulated nucleotide removal. Pretty niche premise but underneath it's just a simulation/greedy problem once you strip away the biology framing.

Questions Asked (1)

Q1

Given a string representing a genome sequence and a character representing a mutated nucleotide, find the earliest time at which no more removals can occur. The mutated nucleotide removes adjacent nucleotides to its left one at a time per unit of time.

Algorithms & Data Structures
Author's notes

The biology wrapper threw me off for a second.

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AI HintsAI Generated

Suggested Approach

Clarify the problem statement to ensure you understand the removal process: the mutated nucleotide removes adjacent nucleotides to its left one at a time per unit of time. Then, model the process as a simulation or derive a formula to compute the earliest time when no more removals can occur, likely by finding the longest contiguous block of non-mutated nucleotides to the left of the mutated one.

Pro tip: After solving, discuss edge cases like when the mutated nucleotide is at the start or end, or when there are multiple mutated nucleotides, and mention how you would test your solution.

1. Clarify the problem

Ask questions to confirm the removal rules: does the mutated nucleotide remove only immediate left neighbors, and does it continue until no left neighbors remain? Confirm if time starts at 0 or 1.

2. Identify the core pattern

Recognize that the process stops when the mutated nucleotide has no more left neighbors to remove, which happens when all nucleotides to its left are removed. The time taken is the number of nucleotides to its left.

3. Handle multiple mutations

If there are multiple mutated nucleotides, determine which one causes the earliest stop. Consider that removals from different mutations might interact, so simulate or compute the maximum contiguous non-mutated segment to the left of any mutation.

4. Derive an efficient algorithm

Propose an O(n) solution: scan the string, track the length of consecutive non-mutated nucleotides before each mutated one, and return the maximum such length (or 0 if none).

5. Test with examples

Walk through small examples, including edge cases (mutation at index 0, all mutations, no mutations), to verify the algorithm and discuss time/space complexity.

Key Points to Mention

  • String traversal and simulation
  • Time complexity analysis (O(n) optimal)
  • Edge cases: mutation at start/end, multiple mutations, no mutations
  • Handling multiple mutated nucleotides and their interactions
  • Space complexity (O(1) if optimized)
  • Potential for using a stack or two-pointer technique

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