JP Morgan·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026

Summary

JP Morgan coding interview, pretty focused on one problem but they pushed hard on follow-ups and complexity trade-offs. Felt like they wanted to see if you actually understood what was happening under the hood, not just whether you could produce working code.

Questions Asked (1)

Given an array of strings, group all strings that are anagrams of each other and return the groups. Be ready to walk through multiple approaches and explain the trade-offs between them.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the sort-and-hash approach, which is the obvious one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., case sensitivity, character set) and then present a brute-force approach followed by optimized solutions using sorting or character frequency counting. Compare trade-offs in time/space complexity and discuss which is preferable in different scenarios, emphasizing the importance of choosing the right data structure.

Pro tip: Mention that in production, you'd consider the input size and memory constraints; for large datasets, the frequency count approach with a hash map is more efficient than sorting each string. Also, note that you can use the frequency count as a key by encoding it into a string or tuple.

1. Clarify requirements and constraints

Ask about input size, character set (ASCII vs Unicode), case sensitivity, and whether the output order matters. This shows attention to detail and helps tailor the solution.

2. Discuss brute-force approach

Explain that you could compare each string with every other string to check if they are anagrams, resulting in O(n^2 * k) time complexity, where n is the number of strings and k is the maximum length. This is inefficient for large inputs.

3. Present optimized approaches

Describe two optimized methods: (1) Sort each string and use the sorted string as a key in a hash map, grouping strings with the same key. (2) Count character frequencies and use the frequency tuple as the key. Compare their time and space complexities.

4. Analyze trade-offs

Compare sorting vs frequency counting: sorting takes O(k log k) per string, while counting takes O(k) but requires a fixed alphabet size. Discuss space complexity and when each is preferable (e.g., sorting is simpler, counting is faster for long strings).

5. Code and test

Write clean code for the chosen approach, handle edge cases (empty strings, single string, no anagrams), and walk through a small example to verify correctness.

Key Points to Mention

Time and space complexity of each approach: brute-force O(n^2 * k), sorting O(n * k log k), frequency counting O(n * k).
Use of hash map to group anagrams, with keys being either sorted strings or frequency tuples.
Trade-offs: sorting is simpler and works for any character set; frequency counting is faster but requires a known alphabet size and may use more space for the count array.
Edge cases: empty input, strings of different lengths, duplicate strings, and Unicode characters.
Potential optimization: use a prime number product as a key (but beware of overflow and collisions).
Real-world considerations: memory usage, streaming input, and parallel processing for very large datasets.

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