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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Microsoft SWE interview that mixed a coding problem into the behavioral portion, which threw me off a bit. The coding section was short, maybe 15-20 minutes, but the question itself had enough wrinkles to keep it interesting.

Questions Asked (1)

Q1

Given a list of business candidates each with a score, distance, and open/closed status, return the top-K entries sorted by score descending, breaking ties by distance ascending.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The sorting logic itself is straightforward but I second-guessed the tie-breaking for a minute.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and constraints (e.g., list size, K, score/distance ranges, status meaning) before proposing a solution. Then discuss a heap-based approach to efficiently find the top-K entries, considering sorting or quickselect alternatives. Finally, analyze time/space complexity and trade-offs, and mention edge cases.

Pro tip: Demonstrate awareness of real-world constraints: if K is small relative to N, a min-heap of size K is optimal; if K is large, sorting might be simpler. Also, clarify whether closed entries should be excluded or included, as this affects the algorithm.

1. Clarify requirements and constraints

Ask about input size, K value, score/distance ranges, and the meaning of open/closed status. Confirm whether closed entries should be filtered out or considered.

2. Choose an efficient algorithm

Propose using a min-heap of size K to track the top-K entries, comparing by score descending and distance ascending. Alternatively, discuss sorting or quickselect based on constraints.

3. Define the comparison logic

Explain how to compare two entries: higher score wins; if scores are equal, smaller distance wins. For a min-heap, the 'worst' entry (lowest score, then largest distance) should be at the top.

4. Analyze complexity and trade-offs

State time complexity: O(N log K) with heap, O(N log N) with sorting. Discuss space complexity and when each approach is preferable.

5. Handle edge cases and finalize

Mention edge cases: K=0, K > N, all closed, ties. Also, consider if the output should be sorted; if so, sort the heap contents at the end.

Key Points to Mention

  • Use a min-heap of size K to efficiently find top-K entries, especially when K is much smaller than N.
  • Comparison logic: sort by score descending, then distance ascending; for min-heap, invert the comparison to keep the worst candidate at the top.
  • Time complexity: O(N log K) for heap approach, O(N log N) for sorting; space complexity O(K) for heap, O(N) for sorting.
  • Filter out closed entries if they should be excluded; clarify this with the interviewer.
  • Edge cases: K=0, K > N, all entries closed, ties in score and distance.
  • If the final output must be sorted, sort the K elements after heap extraction (O(K log K)).

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