Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Microsoft coding screen, one question about streaming token processing. Pretty niche problem that felt more like a parsing puzzle than a traditional algo question.

Questions Asked (1)

Given a stream of strings representing AI output tokens, yield each substring to display in order, stopping at and discarding everything from a special stop token onward (including the rest of the chunk containing it).

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The tricky part is that the stop token can appear mid-chunk, so you can't just skip whole strings.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a stateful streaming solution that processes each chunk incrementally, maintaining a flag to track whether the stop token has been encountered. Discuss trade-offs between simplicity and efficiency, and consider how to handle partial stop tokens across chunk boundaries.

Pro tip: Mention that you would use a buffer to handle stop tokens split across chunks, and discuss the trade-off between buffering and immediate yielding to balance latency and correctness.

1. Clarify Requirements and Edge Cases

Ask about the stop token's nature (single token or sequence), whether it can span multiple chunks, and if the stop token should be excluded from output. Confirm that the stream is processed in order and that no further output is needed after the stop token.

2. Design a Stateful Streaming Algorithm

Propose maintaining a boolean flag 'stopped' and a buffer for partial stop token matches. For each incoming chunk, if not stopped, search for the stop token; if found, yield the substring before it and set stopped to true. Otherwise, yield the chunk (or buffered safe part) and update the buffer.

3. Handle Partial Matches Across Chunks

Explain that when the stop token is a sequence, a suffix of the current chunk might be a prefix of the stop token. Keep that suffix in a buffer and prepend it to the next chunk before searching, to avoid missing a split stop token.

4. Discuss Trade-offs and Optimizations

Compare approaches: simple search per chunk vs. KMP for efficiency. Discuss memory vs. latency trade-offs: buffering the entire stream until stop token vs. yielding incrementally. Mention that for typical AI token streams, chunks are small and stop tokens are short, so a straightforward approach is often sufficient.

5. Test with Examples and Edge Cases

Walk through examples: stop token entirely within one chunk, split across two chunks, at the very beginning, and never appearing. Also consider empty chunks and multiple stop tokens (only first matters).

Key Points to Mention

Stateful streaming: maintain a flag to indicate if stop token has been seen.
Buffer for partial stop token matches across chunk boundaries.
Efficiency: use string search algorithms (e.g., KMP) if stop token is long, but simple search is often fine.
Correctness: ensure stop token is discarded and no output after it.
Edge cases: stop token at start, split across chunks, multiple occurrences.
Trade-offs: latency vs. memory when deciding to buffer or yield immediately.

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