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Amazon·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say
Apr 2026Remote

Summary

Amazon SDE 2 virtual onsite with a coding round that took a weird turn. The interviewer threw in a constraint that felt more like a philosophical debate than an algorithm problem, and I'm still not sure what they were actually testing.

Questions Asked (1)

Q1

Solve Word Ladder II, finding all shortest transformation sequences from a start word to an end word using a given word list. Then, re-solve it without using common data structures like queues, maps, or lists.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Started fine with BFS for shortest paths and backtracking to reconstruct all sequences.

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AI HintsAI Generated

Suggested Approach

Start by explaining the standard BFS + DFS approach for Word Ladder II, focusing on building a graph of shortest paths and then backtracking to find all sequences. Then, for the constraint of no common data structures, describe how you would simulate queues, maps, and lists using arrays and manual indexing, and discuss the trade-offs in time and space complexity.

Pro tip: Emphasize that the core challenge is not just solving the problem but demonstrating adaptability and deep understanding of data structures by re-implementing them from scratch. Also, mention that you would discuss with the interviewer whether the constraint applies to the entire solution or just the main algorithm, as this shows pragmatism.

1. Clarify Requirements and Constraints

Confirm the exact constraints: whether the 'no common data structures' rule applies to the entire solution or just the core algorithm, and whether auxiliary structures like arrays are allowed. Also, clarify input/output formats and edge cases.

2. Explain Standard Solution

Describe the optimal BFS + DFS approach: use BFS to find the shortest distance from start to end and build a graph of predecessors, then use DFS to reconstruct all shortest paths. Mention time and space complexity.

3. Adapt to Constraints

Explain how to replace queues, maps, and lists with arrays and manual indexing. For example, use an array as a queue with head/tail pointers, use parallel arrays for mapping words to indices, and use arrays for adjacency lists.

4. Discuss Trade-offs

Analyze the impact on performance and code complexity: arrays may lead to slower lookups (O(n) instead of O(1)), increased memory usage, and more error-prone code. Discuss whether the constraint is practical and how you would mitigate issues.

5. Test and Validate

Walk through a small example to demonstrate correctness, and discuss how you would test edge cases like no path, multiple paths, and large inputs. Mention potential optimizations within the constraints.

Key Points to Mention

  • BFS for shortest path and DFS for path reconstruction
  • Graph representation using adjacency lists or predecessor maps
  • Simulating queues with arrays and head/tail pointers
  • Simulating hash maps with parallel arrays and linear search
  • Time and space complexity trade-offs when avoiding built-in data structures
  • Edge cases: start == end, no transformation sequence, multiple shortest paths

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