← Microsoft Interview Insights

Microsoft·Software Engineer·Technical Phone Screen·Senior

Senior
Jul 2026

Summary

Microsoft software engineering interview focused entirely on a deep tree data structure design problem. The question had a lot of moving parts and felt more like a take-home assignment crammed into a single session.

Questions Asked (1)

Q1

Design and implement a mutable rooted tree node class in a language of your choice. It should store a value, a parent reference, and an ordered list of children. Include add_child, remove, move_subtree, and replace_child operations with defined time complexities, DFS and BFS iterators, methods for depth, subtree size, and path-to-root, serialization to and from a nested list or JSON, and cycle prevention in the API.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

This was a lot to unpack.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and choosing a language (e.g., Python) that supports clean OOP. Design the node class with parent and children references, implement core operations with explicit time complexities, and ensure cycle prevention by checking ancestry before moves. Then implement iterators, utility methods, and serialization, discussing trade-offs and edge cases.

Pro tip: Proactively discuss time/space trade-offs (e.g., maintaining subtree size for O(1) queries vs. O(n) computation) and mention that cycle prevention is enforced by checking if the target is a descendant before moving. This shows you think about API safety and performance.

1. Clarify requirements and choose language

Confirm the expected operations, serialization format, and any constraints. Choose a language you are comfortable with (e.g., Python) and outline the class structure.

2. Design node structure and core operations

Define the node with value, parent, and children list. Implement add_child, remove, move_subtree, and replace_child, specifying time complexities and ensuring cycle prevention.

3. Implement iterators and utility methods

Write DFS and BFS iterators (iterative to avoid recursion limits). Implement depth, subtree size, and path-to-root, discussing time complexities and possible optimizations.

4. Add serialization and cycle prevention

Implement to/from nested list or JSON. Ensure cycle prevention in move_subtree by checking ancestry, and handle edge cases like moving to self or descendant.

5. Discuss trade-offs and test

Talk about time/space trade-offs (e.g., caching subtree size), potential improvements, and how you would test the implementation.

Key Points to Mention

  • Time complexities: add_child O(1), remove O(1) if parent known, move_subtree O(1) after cycle check, replace_child O(1).
  • Cycle prevention: before moving, check if new parent is in the subtree of the node being moved (e.g., via DFS or ancestor check).
  • Iterators: DFS (pre-order) using stack, BFS using queue; both O(n) time and O(n) space in worst case.
  • Utility methods: depth O(d) where d is depth, subtree size O(n) unless cached, path-to-root O(d).
  • Serialization: nested list or JSON; recursive or iterative; handle cycles if present (but API prevents them).
  • Trade-offs: caching subtree size for O(1) queries vs. O(n) update on modifications; memory vs. speed.

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