Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Bloomberg coding interview, one question the whole session. Classic problem but the full solution has enough moving parts that it's easy to fumble under pressure.

Questions Asked (1)

Given a string and a dictionary of words, return all possible sentences you can form by inserting spaces into the string such that every word appears in the dictionary. Words can be reused.

Algorithms & Data Structures

Author's notes

I knew this problem but blanked on how to handle the memoization cleanly.

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AI HintsAI Generated

Suggested Approach

Use dynamic programming with memoization to avoid recomputing subproblems. Define a recursive function that returns all valid sentences for a given suffix, and combine results by prepending each dictionary word that matches the prefix. Alternatively, use backtracking with memoization to build sentences incrementally.

Pro tip: Mention that the number of possible sentences can be exponential, so the output size itself may be huge; clarify whether the interviewer wants all sentences or just the count. Also, discuss trade-offs between memoization (top-down) and iterative DP (bottom-up) in terms of space and recursion depth.

1. Clarify the problem

Confirm that words can be reused, that the entire string must be segmented, and that the output should be a list of all possible sentences. Ask about constraints like string length and dictionary size.

2. Define the recursive structure

Define a function that returns all valid sentences for a substring starting at index i. For each word in the dictionary, if it matches the prefix at i, recursively get sentences for the remainder and prepend the word.

3. Add memoization

Use a memo table (e.g., array or hash map) to store results for each starting index to avoid recomputing overlapping subproblems. This reduces time complexity from exponential to polynomial in the number of subproblems.

4. Handle base case and build results

If the starting index reaches the end of the string, return a list containing an empty string (representing a valid sentence). Combine results by joining words with spaces.

5. Analyze complexity and edge cases

Discuss time and space complexity: O(n * m * L) where n is string length, m is dictionary size, and L is average word length, but output size can be exponential. Consider edge cases like empty string, no valid segmentation, and words longer than the string.

Key Points to Mention

Dynamic programming with memoization to avoid redundant computations
Backtracking to explore all possible segmentations
Time and space complexity analysis, including exponential output size
Handling of overlapping subproblems and optimal substructure
Edge cases: empty string, no solution, words longer than string
Trade-offs between top-down memoization and bottom-up DP

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