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Early-stage Startup·Software Engineer·Online Assessment (OA)·Junior

JuniorPending
Jun 2026Remote

Summary

Applied for a junior role, was explicitly told the coding portion would be basic Python querying, no leetcode. Then they put a medium-difficulty string manipulation and hash grouping algorithm in front of me. Still processing whether to be annoyed.

Questions Asked (1)

Q1

Given a list of strings, group them by some shared property using a hash-based strategy (e.g. group anagrams or strings with matching character patterns).

Algorithms & Data Structures
Author's notes

Was completely blindsided.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the grouping property and constraints, then propose a hash map where the key is a canonical representation of each string (e.g., sorted characters for anagrams, or a character count signature). Explain how to build the key efficiently and analyze time/space complexity, noting that key generation dominates the runtime.

Pro tip: Mention that for anagram grouping, using a tuple of character counts (size 26) as the key is O(n) per string versus O(n log n) for sorting, which matters at scale. Also note that the hash function must be consistent and collision-resistant for correctness.

1. Clarify the grouping property

Ask the interviewer to confirm the exact shared property (e.g., anagrams, same character set, same pattern) and any constraints on input size, character set, or case sensitivity.

2. Design the canonical key

Choose a representation that uniquely identifies the group, such as sorted string, character count tuple, or normalized pattern. Discuss trade-offs between key generation time and key size.

3. Implement hash-based grouping

Use a hash map (dictionary) to map each key to a list of strings. Iterate through the input, compute the key, and append the string to the corresponding list.

4. Analyze complexity and edge cases

State time complexity O(N * K) where K is key generation cost, and space O(N). Discuss edge cases: empty strings, unicode, large inputs, and hash collisions.

5. Optimize and discuss alternatives

If needed, propose optimizations like using a prime product key for anagrams (with overflow caveats) or a trie for pattern matching. Mention that sorting-based keys are simpler but slower.

Key Points to Mention

  • Hash map (dictionary) as the core data structure for O(1) average-time lookups.
  • Canonical key generation: sorted string vs. character count tuple vs. prime product.
  • Time complexity: O(N * K) where K is the cost to compute the key (e.g., O(L log L) for sorting, O(L) for counting).
  • Space complexity: O(N * L) for storing all strings and keys.
  • Handling edge cases: empty strings, case sensitivity, unicode characters, and hash collisions.
  • Trade-offs between different key strategies and their impact on performance and readability.

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