Capital One·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Capital One software engineer interview with a string manipulation / algorithmic problem that looks deceptively simple but has a real efficiency constraint hiding in it. No behavioral stuff mentioned, just the one coding question.

Questions Asked (1)

Given a list of non-negative integers and a target integer, count all ordered pairs (i, j) where i != j such that concatenating the string representations of numbers[i] and numbers[j] equals the string representation of the target. Aim for better than O(n^2).

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was just brute force every pair, which works but they clearly wanted something faster given the constraint up to 2e5 elements.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and edge cases (e.g., leading zeros, target length). Then, propose a hash map solution that splits the target string into two parts and checks for matching numbers in the list, achieving O(n) time on average. Discuss trade-offs between time and space, and consider alternative approaches like sorting or tries if needed.

Pro tip: Mention that you would handle leading zeros carefully—for example, if a part starts with '0' and has length > 1, it's invalid unless the number itself is '0'. This shows attention to detail and prevents incorrect counts.

1. Clarify requirements and edge cases

Ask about input size, whether numbers can have leading zeros, and if the target can be empty. Confirm that pairs are ordered and i != j.

2. Design hash map approach

Convert the list to a frequency map of string representations. For each possible split of the target into two non-empty parts, check if both parts exist in the map and count valid pairs.

3. Handle special cases

Account for cases where the two parts are identical (need to avoid using the same index twice) and for leading zeros in parts (e.g., '01' is not a valid number unless it's '0').

4. Analyze complexity

Explain that the solution runs in O(n + L) time where L is the length of the target string, and O(n) space for the hash map. This is better than O(n^2).

5. Discuss trade-offs and alternatives

Mention that the hash map approach is optimal for average case, but if memory is constrained, sorting and binary search could be used. Also, note that a trie could be used if many queries are expected.

Key Points to Mention

Hash map for O(1) lookups to avoid nested loops
Splitting the target string into two parts and checking each split
Handling leading zeros and invalid number representations
Avoiding double-counting when the two parts are the same string
Time and space complexity analysis (O(n) time, O(n) space)
Edge cases: empty list, target shorter than any number, numbers with multiple digits

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