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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Two coding questions for a Microsoft SWE round. Both were pretty standard algorithm/design problems but the second one had some tricky edge cases I didn't fully think through in time.

Questions Asked (2)

Q1

Given a list of meeting time intervals, find the minimum number of rooms needed to schedule all meetings without conflicts.

Algorithms & Data Structures
Author's notes

Classic interval scheduling problem.

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AI HintsAI Generated

Suggested Approach

Clarify the problem, then present the sweep line algorithm: separate start and end times, sort them, and use two pointers to count concurrent meetings, tracking the maximum. Alternatively, use a min-heap to track end times. Analyze time and space complexity, and discuss trade-offs.

Pro tip: Mention that the sweep line approach is optimal and can be implemented in O(n log n) time, and that the heap approach is intuitive but may have higher constant factors. Also, discuss how to handle edge cases like empty input or back-to-back meetings.

1. Clarify the problem

Ask if intervals are inclusive/exclusive, if input is sorted, and if we need to return the number or the actual schedule.

2. Choose an approach

Decide between sweep line (sorting starts and ends) or min-heap (sort by start, push end times). Explain why one might be preferred.

3. Walk through the algorithm

Describe step-by-step how to compute the minimum rooms, using a small example to illustrate.

4. Analyze complexity

State time and space complexity: O(n log n) time due to sorting, O(n) space for arrays or heap.

5. Discuss edge cases and optimizations

Mention handling empty input, single meeting, all overlapping, and potential optimizations like using counting sort if times are bounded.

Key Points to Mention

  • Sweep line algorithm with separate sorted start and end times
  • Min-heap approach: sort by start, push end times, pop if room is free
  • Time complexity: O(n log n) due to sorting
  • Space complexity: O(n) for storing start/end arrays or heap
  • Edge cases: empty input, back-to-back meetings, all meetings overlapping
  • Trade-offs: sweep line is simpler and often faster; heap is intuitive but may have overhead

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

Q2

Design a token manager class that supports generating tokens with a TTL, renewing unexpired tokens, and counting currently unexpired tokens.

Algorithms & Data StructuresSystem Design
Author's notes

Trickier than it looks.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a design using a hash map for O(1) token operations and a min-heap or balanced BST for efficient expiration tracking. Discuss trade-offs between lazy and eager expiration, and how to maintain an accurate count of unexpired tokens.

Pro tip: Mention that you would use a min-heap keyed by expiration time to efficiently remove expired tokens, and that lazy deletion can be combined with periodic cleanup to balance performance and memory. Also, highlight the importance of thread safety in a production system.

1. Clarify Requirements

Ask about expected scale, concurrency needs, token format, and whether TTL is fixed or configurable per token. Confirm if tokens are opaque strings or have structure.

2. Design Data Structures

Propose a hash map (token -> expiration time) for O(1) lookup and renewal, and a min-heap (expiration time -> token) for efficient expiration. Discuss alternatives like a balanced BST or time-wheel.

3. Implement Core Operations

Outline methods: generateToken(ttl) creates a token, stores it, and adds to heap; renewToken(token, newTtl) checks expiration, updates map and heap; countUnexpired() returns size after cleaning expired tokens.

4. Handle Expiration and Counting

Explain lazy expiration: on count or renew, pop expired tokens from heap and remove from map. Discuss periodic cleanup or eager expiration for real-time accuracy.

5. Address Concurrency and Edge Cases

Mention thread safety via locks or concurrent data structures. Cover edge cases: token renewal after expiry, duplicate tokens, clock skew, and memory management.

Key Points to Mention

  • Use a hash map for O(1) token lookup and renewal.
  • Use a min-heap keyed by expiration time for efficient expiration.
  • Lazy expiration: clean up expired tokens during count or renewal operations.
  • Trade-offs: lazy vs. eager expiration, memory vs. accuracy.
  • Thread safety: use locks or concurrent data structures for multi-threaded environments.
  • Token generation: use secure random or UUID, and consider collision resistance.

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