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Microsoft·Software Engineer·Technical Phone Screen·Senior

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Apr 2026

Summary

Microsoft SWE interview with a meaty algorithmic problem that mixed string parsing, DP, and trie/hashing design tradeoffs. The question had several layers and the complexity analysis portion caught me a bit flat-footed.

Questions Asked (1)

Q1

Given a string s and a dictionary of tokens, determine if s can be broken into a sequence of one or more dictionary words. If yes, return one valid segmentation. Your solution should handle s and dict both up to 100,000 entries. Discuss trie or hash-based prefix pruning, DP with early exits, time and space complexity, and how you'd extend the approach to count all distinct segmentations modulo 1e9+7.

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

This is basically word break but they wanted the full treatment.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then present a DP solution with a trie or hash set for O(1) word lookups, using prefix pruning to skip invalid starts. Discuss time/space complexity and how to extend to counting segmentations with modulo arithmetic.

Pro tip: Mention that early exits and pruning are crucial for large inputs, and that using a trie can reduce unnecessary substring checks, especially when the dictionary has many words with common prefixes.

1. Clarify requirements and constraints

Confirm that s and dict can be up to 100,000 entries, and that we need one valid segmentation or indication of impossibility. Ask about character set, case sensitivity, and whether empty strings are allowed.

2. Choose data structures for efficient lookup

Use a hash set for O(1) word lookups, or a trie to enable prefix pruning and reduce unnecessary substring checks. Discuss trade-offs: hash set is simpler but may check many substrings; trie can prune early.

3. Design DP with early exits

Define dp[i] as whether s[0:i] can be segmented. Iterate i from 1 to n, and for each j < i, if dp[j] and s[j:i] in dict, set dp[i]=True and store parent pointer. Use early exit when a valid segmentation is found.

4. Analyze complexity and optimize

Time: O(n^2) worst-case with hash set, but with trie and pruning it can be closer to O(n * maxWordLength). Space: O(n) for DP and O(total characters) for trie. Mention that early exits can significantly reduce runtime in practice.

5. Extend to counting all segmentations

Modify DP to count ways: dp[i] = sum(dp[j] for j < i if s[j:i] in dict) mod 1e9+7. Use the same trie/hash set for lookups, and note that counting requires exploring all valid j, so early exits are not used.

Key Points to Mention

  • Use a trie or hash set for O(1) average word lookup; trie enables prefix pruning to skip invalid substrings early.
  • DP with early exits: stop as soon as a valid segmentation is found to save time.
  • Time complexity: O(n^2) worst-case with hash set, but can be optimized to O(n * L) with trie where L is max word length.
  • Space complexity: O(n) for DP array and O(total characters) for trie or O(number of words) for hash set.
  • For counting all segmentations, use DP with modulo 1e9+7 and sum over all valid j; no early exits.
  • Handle edge cases: empty string, no segmentation possible, and very long strings requiring memory-efficient structures.

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