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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Meta coding screen, pretty much just the one problem the whole time. Nothing fancy about the setup but the question had more edge cases than I expected going in.

Questions Asked (1)

Q1

Given an array of integers and a value k, find all pairs of elements whose difference equals k.

Algorithms & Data Structures
Author's notes

Started with the brute force two-pointer approach and the interviewer just kind of waited.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: whether pairs are ordered, if duplicates are allowed, and if the array is sorted. Then propose an efficient solution using a hash set to track seen elements, checking for both x + k and x - k to handle absolute difference. Discuss time and space complexity, and consider edge cases like k = 0 and negative numbers.

Pro tip: Mention that if the array is sorted, a two-pointer approach can find pairs in O(n) time with O(1) extra space, but sorting takes O(n log n). This shows you understand trade-offs and can adapt to constraints.

1. Clarify requirements

Ask if the array is sorted, if pairs are ordered, if duplicates should be counted, and if k can be zero or negative. This ensures you solve the correct problem.

2. Choose an approach

Decide between hash set (O(n) time, O(n) space) and sorting + two pointers (O(n log n) time, O(1) space). Explain your choice based on constraints.

3. Outline the algorithm

For hash set: iterate through array, for each element check if element + k or element - k exists in set, then add element to set. For two pointers: sort array, use left and right pointers to find pairs with difference k.

4. Analyze complexity and edge cases

State time and space complexity. Discuss edge cases: empty array, k = 0 (need to handle duplicates), negative numbers, and large k.

5. Test with examples

Walk through a small example to verify correctness, such as array [1, 5, 3, 4, 2] and k = 2, showing pairs (1,3), (3,5), (2,4).

Key Points to Mention

  • Hash set approach for O(n) time complexity
  • Two-pointer approach after sorting for O(n log n) time and O(1) space
  • Handling k = 0 by counting frequencies or using a set to avoid duplicate pairs
  • Avoiding duplicate pairs by only checking one direction (e.g., if x + k exists) or using a set of pairs
  • Time and space complexity analysis
  • Edge cases: empty array, k negative, large array

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