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Microsoft·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Microsoft SWE interview that went deep on string processing fundamentals. One question, but it had a lot of layers: tokenization design, implementation, streaming, testing, and complexity analysis all rolled in.

Questions Asked (1)

Q1

Given a text document, count the number of words. Before coding, define your tokenization rules precisely (contractions, hyphenated words, decimals, punctuation, Unicode quotes, whitespace), then implement a function that handles large files or streams, write unit tests for edge cases, and analyze the time/space complexity. Also compare regex-based tokenization against a manual character scanner.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

This looked like a warm-up question until it wasn't.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining tokenization rules for edge cases like contractions, hyphenated words, decimals, and Unicode punctuation, then present a streaming implementation that processes input in chunks to handle large files. Compare regex-based and manual scanner approaches, discussing trade-offs in performance, readability, and correctness, and include unit tests for edge cases and complexity analysis.

Pro tip: Demonstrate production awareness by discussing memory constraints and proposing a streaming solution with a fixed-size buffer, and mention that regex can be compiled once for efficiency but may have catastrophic backtracking risks on adversarial input.

1. Define Tokenization Rules

Explicitly state rules for what constitutes a word: how to handle contractions (e.g., 'don't' as one or two words), hyphenated words (e.g., 'state-of-the-art'), decimals (e.g., '3.14'), punctuation, Unicode quotes, and whitespace. Clarify assumptions and edge cases.

2. Design Streaming Implementation

Propose a function that reads input in chunks (e.g., using a generator or buffer) to handle large files without loading everything into memory. Maintain state across chunks to correctly tokenize words that span chunk boundaries.

3. Compare Regex vs Manual Scanner

Discuss regex-based tokenization (e.g., using \b or Unicode-aware patterns) versus a manual character scanner. Analyze trade-offs: regex is concise but may be slower or risk backtracking; manual scanner is more control but verbose.

4. Write Unit Tests for Edge Cases

List key edge cases to test: empty input, multiple spaces, leading/trailing whitespace, contractions, hyphenated words, decimals, Unicode quotes, and mixed punctuation. Ensure tests cover both regex and manual approaches if implemented.

5. Analyze Complexity and Optimize

State time complexity O(n) for both approaches, where n is input size, and space complexity O(1) for streaming (excluding input buffer) or O(k) for regex matches. Discuss potential optimizations like compiled regex or avoiding unnecessary string allocations.

Key Points to Mention

  • Tokenization rules must be explicitly defined and justified based on use case (e.g., natural language processing vs. simple word count).
  • Streaming with a fixed-size buffer ensures O(1) auxiliary space and handles files larger than memory.
  • Regex can be efficient if precompiled and anchored, but may suffer from catastrophic backtracking on certain patterns; manual scanner gives precise control.
  • Unicode handling: use Unicode-aware regex (e.g., \p{L}) or iterate over code points to correctly handle non-ASCII letters and quotes.
  • Unit tests should cover boundary conditions: empty input, single word, punctuation-only, mixed scripts, and chunk boundaries.
  • Time complexity is O(n) for both approaches; space complexity depends on buffering strategy and whether matches are stored.

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