← Amazon Interview Insights

Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Amazon SWE coding round, one meaty string problem that looked like a sliding window thing but had enough edge cases to keep me busy for the whole session. The discussion portion on complexity and unicode caught me slightly off guard.

Questions Asked (1)

Q1

Given a string and a list of words, find the starting index of any substring that is a concatenation of one or more of those words (words can repeat and appear in any order). Return -1 if no such substring exists. Implement this, then walk through time/space complexity and edge cases like overlapping matches, large dictionaries, and unicode strings.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I jumped straight to a sliding window approach and it mostly held up, but then they pushed on variable word lengths and my window logic started falling apart.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., word length uniformity, string size) and then propose a sliding window approach using a hash map to track word frequencies. Implement the solution, then analyze time/space complexity and discuss edge cases like overlapping matches, large dictionaries, and unicode handling.

Pro tip: Emphasize that all words must be the same length for the concatenation to be valid; this simplifies the sliding window and shows you understand the problem's core constraint. Also, mention that you would test with unicode strings to ensure proper handling of multi-byte characters.

1. Clarify requirements and constraints

Ask about input sizes, word length uniformity, character encoding, and whether overlapping matches should be considered. Confirm that words can repeat and appear in any order.

2. Design the algorithm

Use a sliding window of size total_words * word_length. For each starting index, check if the substring can be segmented into valid words using a frequency map. Optimize by skipping indices that are not multiples of word length when words are uniform.

3. Implement the solution

Write clean code with helper functions. Use a hash map to count word frequencies, and a temporary map to track seen words in the current window. Iterate over possible starting positions and return the first valid index.

4. Analyze complexity and edge cases

State time complexity: O(N * M) where N is string length and M is number of words, or O(N) with optimizations. Space complexity: O(M) for the frequency map. Discuss edge cases: empty string, no match, overlapping matches, large dictionary, unicode strings.

5. Test and validate

Walk through examples, including edge cases. Mention testing with unicode strings to ensure proper handling of multi-byte characters. Consider performance for large inputs and suggest possible optimizations.

Key Points to Mention

  • Uniform word length is crucial for the sliding window approach; if words vary in length, the problem becomes more complex.
  • Use a hash map to store word frequencies and a temporary map for the current window to efficiently check valid concatenations.
  • Time complexity: O(N * M) where N is string length and M is number of words, but can be optimized to O(N) by skipping non-aligned indices.
  • Space complexity: O(M) for the frequency map, where M is the number of unique words.
  • Edge cases: overlapping matches (e.g., 'aaaa' with words ['aa', 'aa']), large dictionaries (memory and lookup efficiency), and unicode strings (ensure proper character handling).
  • Consider using a trie for large dictionaries to optimize word lookups, but note that hash map is simpler and often sufficient.

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