Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Amazon SWE coding round, one meaty string problem that branched into a full design discussion. More involved than I expected for a single question session.

Questions Asked (1)

Given an array of up to 100,000 lowercase strings, return all strings that can be formed by concatenating at least two other strings from the same array. Component strings can be reused. Walk through two implementation approaches, analyze complexity, and handle edge cases like short words, repeated components, and large inputs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the hash-set plus DP approach because it felt more approachable.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present two approaches: a brute-force pairwise concatenation with a hash set for O(n^2 * L) time, and an optimized DP-based solution using a trie or hash set for O(n * L^2) time. Analyze time and space complexity for each, and discuss trade-offs such as memory usage and practical performance on large inputs.

Pro tip: Mention that sorting strings by length and processing from shortest to longest can avoid redundant checks, and that using a trie can prune searches early, which is crucial for Amazon-scale data.

1. Clarify requirements and edge cases

Ask about input constraints, definition of 'other strings' (can a string be concatenated with itself?), and whether the result should be unique. Discuss edge cases like empty strings, single-character strings, and strings that are themselves components of others.

2. Present brute-force approach

Describe generating all possible concatenations of two strings from the array, checking if each result exists in the set, and collecting valid ones. Analyze complexity: O(n^2 * L) time and O(n * L) space, noting it's impractical for n=100,000.

3. Present optimized DP/trie approach

Explain using a hash set or trie for O(1) or O(L) lookups, then for each string, use dynamic programming to check if it can be segmented into at least two words from the set. Complexity: O(n * L^2) time with hash set or O(n * L) with trie, and O(n * L) space.

4. Compare trade-offs and handle edge cases

Discuss when to use each approach, memory vs. speed trade-offs, and how to handle repeated components (e.g., 'a' + 'a' = 'aa') and short words. Mention that sorting by length can optimize by skipping longer strings early.

5. Summarize and conclude

Reiterate the chosen approach, its complexity, and why it's suitable for large inputs. Emphasize correctness and efficiency.

Key Points to Mention

Use a hash set for O(1) lookups to check if a substring is a valid word.
Dynamic programming to determine if a string can be segmented into at least two words from the set.
Trie data structure for efficient prefix matching and early pruning.
Time complexity: O(n * L^2) with hash set, O(n * L) with trie; space complexity: O(n * L).
Edge cases: empty strings, single-character strings, strings that are concatenations of themselves, and duplicate results.
Optimization: sort strings by length and process shorter strings first to avoid unnecessary checks.

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