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Capital One·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Capital One SWE coding question, pretty focused on string manipulation and prefix logic. Nothing too wild but the edge cases will get you if you're not careful.

Questions Asked (1)

Q1

Given a list of words, count the number of pairs (i, j) where one word equals the other, or one word is a prefix of the other.

Algorithms & Data Structures
Author's notes

My first instinct was brute force, just check every pair, which technically works but felt embarrassing to say out loud.

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AI HintsAI Generated

Suggested Approach

Clarify the definition of a 'pair' (ordered vs unordered) and whether duplicates count. Then propose a trie-based solution that inserts words and counts prefix matches, or a sorting-based approach that groups words by prefix. Discuss time/space complexity and handle edge cases like empty strings.

Pro tip: Mention that the trie approach can be optimized by storing the count of words passing through each node, allowing O(L) per word insertion and O(L) per word query for prefix matches. This shows you understand both the algorithm and its practical efficiency.

1. Clarify the problem

Ask whether pairs are ordered (i,j) or unordered, and whether a word is considered a prefix of itself. Confirm if the list can contain duplicates and if empty strings are allowed.

2. Discuss brute force and its complexity

Mention the naive O(N^2 * L) approach of comparing each pair, and explain why it's inefficient for large inputs.

3. Propose an efficient approach

Describe a trie-based solution: insert all words into a trie, and for each word, traverse the trie to count words that are prefixes or equal. Alternatively, sort the list and use binary search or two-pointer technique.

4. Analyze time and space complexity

For trie: O(N * L) time and O(N * L) space, where N is number of words and L is average length. For sorting: O(N log N * L) time and O(1) extra space (if in-place).

5. Handle edge cases and test

Consider empty list, single word, all words identical, words with common prefixes, and very long words. Walk through a small example to verify correctness.

Key Points to Mention

  • Definition of prefix: a string is a prefix of itself, so equal words count as prefix pairs.
  • Trie data structure: nodes store children and a count of words ending or passing through.
  • Counting pairs: for each word, the number of valid pairs is the count of words that are prefixes (including itself) minus 1 (to exclude self-pair if unordered).
  • Time complexity: O(N * L) for trie insertion and query, which is optimal for this problem.
  • Space complexity: O(N * L) for trie, which may be high but acceptable for typical constraints.
  • Alternative approach: sort words lexicographically and use binary search to find words with the current word as prefix.

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