Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Google SWE interview with a follow-up problem similar to Meeting Rooms III. Nothing too crazy, just needed to walk through an example and the interviewer seemed satisfied.

Questions Asked (1)

Given a set of meeting time intervals, find the minimum number of conference rooms required (similar to the Meeting Rooms III variant).

Algorithms & Data Structures

Author's notes

Dry-running through an example was basically all they wanted.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then discuss the sweep line algorithm using a min-heap to track end times. Alternatively, present the two-pointer approach with sorted start and end times, and analyze time/space complexity.

Pro tip: Mention that the problem is equivalent to finding the maximum number of overlapping intervals at any point, and that the heap approach naturally handles the Meeting Rooms III variant where rooms are assigned to meetings with the earliest end time.

1. Clarify requirements and edge cases

Ask about input format, whether intervals are inclusive/exclusive, if meetings can be back-to-back, and if the number of rooms is fixed (Meeting Rooms III).

2. Propose a brute-force baseline

Briefly mention a simple O(n^2) approach that checks each meeting against all others to count overlaps, to establish a starting point.

3. Present the optimal sweep line with min-heap

Sort meetings by start time, use a min-heap to track end times of ongoing meetings, and for each meeting, remove ended meetings and add the new end time; the heap size is the room count.

4. Discuss alternative two-pointer approach

Sort start and end times separately, use two pointers to count active meetings, incrementing on start and decrementing on end, tracking the maximum.

5. Analyze complexity and handle variants

State time O(n log n) and space O(n), and explain how to adapt for Meeting Rooms III (e.g., using a priority queue of rooms by end time and room number).

Key Points to Mention

Sorting intervals by start time is crucial for both approaches.
Min-heap efficiently tracks the earliest ending meeting to free a room.
The maximum number of concurrent meetings equals the minimum rooms needed.
Two-pointer method avoids heap but requires sorting start and end times separately.
Time complexity is O(n log n) due to sorting, space O(n) for heap or arrays.
For Meeting Rooms III, use a priority queue of rooms ordered by end time and room index to assign the earliest available room.

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