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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Microsoft SWE interview with a scheduling/intervals problem that's pretty much a classic at this point. Nothing shocking but it's the kind of question that punishes you if you haven't drilled the heap-based approach.

Questions Asked (1)

Q1

Given an array of meeting time intervals, what is the minimum number of conference rooms needed so that no two overlapping meetings are scheduled in the same room?

Algorithms & Data Structures
Author's notes

I knew the problem but fumbled the implementation a bit.

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AI HintsAI Generated

Suggested Approach

Clarify the problem and edge cases, then explain that the minimum number of rooms equals the maximum number of overlapping meetings at any point in time. Present the sweep line algorithm: separate start and end times, sort them, and use two pointers to count active meetings, tracking the maximum.

Pro tip: Mention that this is equivalent to finding the maximum clique in an interval graph, which can be solved in O(n log n) by sorting. Also, discuss how to handle edge cases like zero-length meetings and whether intervals are inclusive/exclusive.

1. Clarify the problem

Ask about input format, edge cases (empty array, zero-length meetings), and whether intervals are half-open or closed. Confirm that overlapping means any intersection, including touching endpoints if inclusive.

2. Identify the core insight

The minimum number of rooms needed is the maximum number of concurrent meetings. This can be found by sweeping through time and counting active intervals.

3. Design the algorithm

Extract all start and end times into separate arrays, sort both. Use two pointers to simulate a timeline: increment count when a start is encountered, decrement when an end is encountered, and track the maximum count.

4. Analyze complexity and edge cases

Time complexity is O(n log n) due to sorting; space is O(n). Handle edge cases: empty input returns 0, meetings that end exactly when another starts may or may not overlap depending on definition.

5. Discuss alternatives and optimizations

Mention a min-heap approach: sort by start time, push end times into a heap, and for each meeting, if the earliest end time is <= current start, pop and reuse the room. This also runs in O(n log n) and may be more intuitive.

Key Points to Mention

  • The problem reduces to finding the maximum number of overlapping intervals.
  • Sweep line algorithm with sorted start and end times.
  • Time complexity O(n log n) and space O(n).
  • Edge cases: empty input, zero-length meetings, and inclusive/exclusive endpoints.
  • Alternative min-heap approach for room reuse.
  • Connection to interval graph coloring and maximum clique.

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