Capital One·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Capital One SWE interview with a data structure design problem that looked straightforward but had a subtle duplicate-counting gotcha. The core challenge was around efficiently maintaining two lists and supporting updates while keeping pair counts correct.

Questions Asked (1)

You have two lists A and B. Support two operations: update an element in B to a new value, and given a target t, count the number of index pairs (i, j) such that A[i] + B[j] equals t. Duplicates in either list count as distinct pairs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was a hash set and I immediately lost the duplicate case.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (list sizes, update frequency, query frequency) and propose a solution that balances update and query costs. A common approach is to maintain a frequency map of B and use it to answer queries in O(|A|) time, with O(1) updates. For large lists, consider trade-offs like precomputing sums or using a balanced BST.

Pro tip: Always discuss the trade-off between update and query time, and mention that the optimal choice depends on the ratio of updates to queries. This shows you think about real-world performance, not just theoretical complexity.

1. Clarify requirements and constraints

Ask about the sizes of A and B, the number of operations, and whether updates and queries are interleaved. This determines the acceptable time complexity.

2. Propose a baseline solution

Maintain a frequency map (hash map) of B. For each query, iterate through A and for each a, add freq[t - a] to the count. Updates are O(1) by adjusting the frequency map.

3. Analyze time complexity

Query time is O(|A|), update time is O(1), space is O(|B|). Discuss if this is acceptable given constraints.

4. Discuss optimizations and trade-offs

If queries are frequent and A is large, consider precomputing all possible sums or using a balanced BST for B to answer queries faster. Mention that precomputation may be costly if updates are frequent.

5. Handle duplicates and edge cases

Ensure duplicates are counted correctly by using frequencies. Consider empty lists, negative numbers, and integer overflow.

Key Points to Mention

Use a hash map to store frequencies of elements in B for O(1) updates and O(|A|) queries.
Time complexity: O(|A|) per query, O(1) per update, O(|B|) space.
Trade-off: If updates are rare, precomputing all sums can answer queries in O(1) but updates become O(|A|).
Duplicates are handled by counting frequencies, not just presence.
Edge cases: empty lists, target not found, large values causing overflow.
Alternative: If B is static, sort B and use binary search for each a, but updates would require re-sorting.

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