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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Microsoft SWE interview, technical phone screen focused on a trie-based autocomplete design problem. Pretty implementation-heavy for a phone screen, but the follow-up on complexity analysis is what actually tripped me up.

Questions Asked (1)

Q1

Implement a simplified autocomplete system: given a list of historical sentences, build a class that takes one character at a time and returns all sentences matching the current prefix. When the user types '#', add the completed sentence to the dictionary and reset the input. No need to rank results by frequency.

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

I jumped straight to a trie which felt right, and the interviewer seemed fine with that direction.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints, then propose a trie-based solution where each node stores a set of sentence indices. Explain how to handle character input, prefix matching, and sentence insertion when '#' is typed, and discuss trade-offs between different data structures.

Pro tip: Mention that storing sentence indices at each node allows efficient retrieval without duplicating strings, and that using a set ensures uniqueness. Also, note that if ranking by frequency were required, you could store counts at the terminal nodes.

1. Clarify Requirements and Constraints

Ask about input size, character set, expected query frequency, and whether sentences can be duplicates. Confirm that results need not be ranked.

2. Choose Data Structure

Propose a trie (prefix tree) where each node represents a character and stores a set of sentence indices. Alternatively, discuss a sorted list with binary search for prefix ranges.

3. Design Operations

For input(c): if c == '#', add the current sentence to the trie and reset; else, traverse the trie with the current prefix and return all sentences in the subtree.

4. Analyze Complexity and Trade-offs

Compare trie vs. sorted list: trie offers O(prefix length + output size) query time but higher memory; sorted list uses less memory but O(log n + output size) query time with binary search.

5. Handle Edge Cases and Optimizations

Discuss empty prefix, duplicate sentences, memory optimization (e.g., storing indices instead of strings), and potential concurrency if needed.

Key Points to Mention

  • Trie data structure for efficient prefix matching
  • Storing sentence indices at nodes to avoid string duplication
  • Handling '#' to insert and reset input
  • Time complexity: O(prefix length + number of matches) for queries
  • Space complexity: O(total characters in all sentences)
  • Trade-offs between trie and sorted list with binary search

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