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Early-stage Startup·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Short technical screen, basically just one coding problem about merging K arrays. Not much else to report.

Questions Asked (1)

Q1

Given K sorted arrays, merge them into a single sorted array.

Algorithms & Data Structures
Author's notes

Classic heap problem but I fumbled the implementation a bit under pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., K and total elements) and discussing naive approaches before optimizing. Then present a min-heap solution that efficiently merges the arrays in O(N log K) time, and walk through the algorithm with a small example.

Pro tip: Mention that for small K or when arrays are on disk, a divide-and-conquer merge might be more cache-friendly, showing you consider practical trade-offs beyond just time complexity.

1. Clarify constraints and edge cases

Ask about the number of arrays (K), total elements (N), data types, memory limits, and whether arrays can be empty. This ensures you tailor the solution to the actual problem.

2. Discuss naive approaches

Mention that concatenating and sorting takes O(N log N) time, which is suboptimal. Also note that merging arrays one by one can be O(N*K) in the worst case.

3. Present the optimal min-heap solution

Explain how to use a min-heap of size K to repeatedly extract the smallest element and insert the next from the same array. This yields O(N log K) time and O(K) extra space.

4. Walk through an example

Trace the algorithm on a small example (e.g., 3 arrays) to demonstrate correctness and help the interviewer follow your reasoning.

5. Analyze complexity and discuss alternatives

State time and space complexity clearly. Optionally, mention divide-and-conquer merge as an alternative with O(N log K) time but different constant factors.

Key Points to Mention

  • Min-heap (priority queue) of size K to track the smallest current element from each array.
  • Time complexity: O(N log K) where N is total elements and K is number of arrays.
  • Space complexity: O(K) for the heap (plus O(N) for the output).
  • Handling edge cases: empty arrays, K=0, K=1, or arrays of different lengths.
  • Comparison with naive approaches: concatenate+sort (O(N log N)) and iterative merging (O(N*K)).
  • Divide-and-conquer merge as an alternative with similar complexity but potentially better cache performance.

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