Visa·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Visa SWE interview had at least one algorithmic problem that looked straightforward on the surface but had a non-obvious optimal approach. The kind of question where you can get a working solution pretty easily but the interviewer clearly wants something better.

Questions Asked (1)

Given a sorted integer array, an integer k, and a target value x, return the k integers from the array that are closest to x, sorted in ascending order. If two numbers are equally close, prefer the smaller one.

Algorithms & Data Structures

Author's notes

I got a working solution pretty fast using a sorted approach but the interviewer kept pushing on time complexity.

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AI HintsAI Generated

Suggested Approach

Use binary search to find the insertion point of x, then use two pointers to expand outward and collect the k closest elements. Alternatively, use a sliding window of size k and binary search for the optimal starting index. Return the result in ascending order.

Pro tip: Clarify tie-breaking rules upfront and mention that the sorted array allows O(log n + k) time, which is optimal. Also, discuss edge cases like k equal to array length or x outside the array range.

1. Understand the problem and constraints

Restate the problem to ensure clarity: given a sorted array, find k closest integers to x, with ties broken by preferring the smaller number. Confirm input sizes and expected time complexity.

2. Choose an efficient approach

Decide between binary search + two pointers or sliding window. Explain why O(log n + k) is achievable and better than sorting by distance (O(n log n)).

3. Implement the algorithm

For binary search + two pointers: find the insertion point, then compare distances and move pointers inward. For sliding window: binary search for the left bound of the window of size k.

4. Handle edge cases and ties

Ensure correct behavior when x is smaller than all elements, larger than all, or when k equals the array length. Apply tie-breaking rule: if distances are equal, choose the smaller element.

5. Analyze complexity and test

State time and space complexity. Walk through a few test cases, including duplicates and negative numbers, to verify correctness.

Key Points to Mention

Binary search to find the closest element or insertion point
Two-pointer technique to expand and select k closest elements
Sliding window of size k with binary search for optimal start
Tie-breaking rule: prefer smaller element when distances are equal
Time complexity: O(log n + k) and space complexity: O(1) extra (excluding output)
Edge cases: k = 0, k = n, x outside array range, duplicates in array

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