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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Microsoft SWE interview with a streaming/parsing problem that had a clean easy version and a nastier follow-up. Pretty focused session, just the one problem but it went in directions I didn't fully anticipate.

Questions Asked (2)

Q1

You're receiving a stream of string chunks. Concatenate them and output every character up until a designated stop token appears. Once the stop token is seen, stop outputting. The stop token is not included in the output. For this version, assume the stop token always arrives fully contained within a single chunk.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The basic version felt manageable.

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AI HintsAI Generated

Suggested Approach

Clarify the streaming constraints and the guarantee that the stop token arrives fully within a single chunk. Then design a stateful solution that buffers only the minimum necessary to detect the stop token across chunk boundaries, and discuss trade-offs between simplicity and memory efficiency.

Pro tip: Explicitly call out that the guarantee simplifies the problem but you would still design for the general case where the stop token can span chunks, showing foresight and robustness.

1. Clarify requirements and constraints

Ask about chunk size, stop token length, whether the stop token can appear multiple times, and what to do if it never appears. Confirm the guarantee that the stop token is fully contained in one chunk.

2. Design a stateful streaming algorithm

Maintain a buffer of the last (stop_token_length - 1) characters to handle potential partial matches across chunks. For each chunk, search for the stop token; if found, output up to its start and stop; otherwise, output all but the last (stop_token_length - 1) characters and buffer the rest.

3. Handle edge cases and termination

Consider cases where the stop token is at the beginning or end of a chunk, multiple stop tokens, and the stream ending without a stop token. Ensure the buffered characters are flushed if the stream ends without the stop token.

4. Analyze complexity and trade-offs

Discuss time complexity O(n) where n is total characters, and space complexity O(k) where k is stop token length. Compare with simpler approaches that concatenate all chunks first, highlighting memory efficiency.

5. Test with examples

Walk through a concrete example, such as chunks ['ab', 'cde', 'fgh'] with stop token 'def', showing how the buffer and output evolve.

Key Points to Mention

  • Streaming processing avoids storing the entire input in memory, which is crucial for large or infinite streams.
  • The buffer size is bounded by the stop token length minus one, ensuring O(k) auxiliary space.
  • The guarantee that the stop token is fully contained in a chunk simplifies detection but the algorithm can be extended to handle cross-chunk tokens.
  • Time complexity is linear in the total number of characters processed.
  • Edge cases: stop token at the very beginning, multiple stop tokens, and stream termination without stop token.
  • Trade-off between simplicity (concatenate all then search) and efficiency (streaming with buffer).

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

Q2

Follow-up to the above: what if the stop token can span across multiple chunks? How would you handle that case?

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

This is where I got a bit turned around.

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AI HintsAI Generated

Suggested Approach

Acknowledge that stop tokens can span chunk boundaries and propose a buffering strategy that retains a suffix of the stream to detect multi-chunk tokens. Discuss trade-offs between buffer size, latency, and memory, and mention how to handle false positives and edge cases.

Pro tip: Mention that the buffer size should be at least the maximum stop token length minus one, and that you can use a rolling hash or KMP for efficient matching. Also note that you should flush the buffer only when no partial match is possible.

1. Identify the problem

Explain that stop tokens may be split across chunks, so naive per-chunk matching fails. Emphasize the need to maintain state across chunks.

2. Buffer strategy

Propose keeping a buffer of the last N-1 characters (where N is the max stop token length) and prepending it to each new chunk before searching for stop tokens.

3. Efficient matching

Use a multi-pattern string matching algorithm (e.g., Aho-Corasick) or a rolling hash to detect stop tokens across the buffer and chunk efficiently.

4. Handle matches and false positives

When a match is found, truncate the output at the match start and stop processing. If a partial match is at the end of the buffer, retain it for the next chunk.

5. Trade-offs and optimizations

Discuss memory vs latency trade-offs: larger buffer increases memory but reduces risk of missing tokens. Mention that you can flush the buffer when no partial match is possible to reduce latency.

Key Points to Mention

  • Buffer size should be max_stop_token_length - 1 to catch all cross-chunk tokens.
  • Use efficient multi-pattern matching like Aho-Corasick or KMP to avoid O(n*m) per chunk.
  • Maintain state across chunks: keep a suffix buffer and a flag for partial matches.
  • Handle false positives: ensure that a match is a complete stop token, not a substring of a larger token.
  • Consider streaming constraints: flush buffer only when safe to minimize latency.
  • Edge cases: stop token longer than chunk size, overlapping stop tokens, and empty chunks.

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