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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Microsoft SWE interview with a tree traversal follow-up that only one candidate in the session even got to. Ran out of time before finding out if there were more questions after it, which was a little unsettling.

Questions Asked (1)

Q1

Given a table representing a company hierarchy (where each manager record includes a list of their direct reports), write a function to find all reports under a given person at any depth.

Algorithms & Data StructuresData Modeling
Author's notes

Went with BFS, which felt right for level-by-level traversal of a tree structure.

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AI HintsAI Generated

Suggested Approach

Model the hierarchy as a directed graph where each person is a node and manager-to-report relationships are edges. Then perform a traversal (DFS or BFS) starting from the given person, collecting all reachable nodes except the start. Discuss handling cycles and choosing between recursion and iteration.

Pro tip: Clarify whether the hierarchy is a tree or a general graph—if cycles are possible, you must track visited nodes to avoid infinite loops. Also, mention that the function should return unique reports and consider the time/space complexity.

1. Clarify the data structure and requirements

Ask whether the hierarchy is a tree (no cycles) or a general graph, and whether the input is given as an adjacency list, nested objects, or a database table. Confirm that the output should be all direct and indirect reports, excluding the person themselves.

2. Choose a traversal strategy

Decide between DFS (recursive or iterative with a stack) and BFS (with a queue). Both work; DFS is often simpler to code recursively, while BFS naturally returns reports level by level.

3. Implement the traversal with cycle handling

Write the function, using a visited set to avoid revisiting nodes if cycles are possible. For each node, add its direct reports to the result and continue traversal from them.

4. Analyze complexity and edge cases

State that time complexity is O(N + E) where N is number of people and E is number of reporting relationships, and space is O(N) for the visited set and result. Discuss edge cases: person not found, no reports, deep hierarchy causing stack overflow (if recursive).

5. Test with examples

Walk through a small example, such as a CEO with two managers, each with two reports, and verify the output includes all four reports. Also test a cycle if applicable.

Key Points to Mention

  • Graph traversal algorithms: DFS and BFS
  • Handling cycles with a visited set
  • Time and space complexity analysis
  • Recursive vs iterative implementation trade-offs
  • Edge cases: empty input, person not found, self-referencing loops
  • Data modeling: adjacency list vs nested objects vs database table

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