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Walmart·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediateRejected
Apr 2026

Summary

Third round interview, got a trie implementation question with about 20-25 minutes on the clock and completely fell apart. Knew the data structure, had used it before, just couldn't pull it together when it counted.

Questions Asked (1)

Q1

Implement a trie data structure from scratch.

Algorithms & Data Structures
Author's notes

This is the one that got me and I still can't fully explain why.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and use cases for the trie, then design the node structure and operations (insert, search, startsWith). Implement the trie with clean, efficient code, and discuss time/space complexity and potential optimizations.

Pro tip: Demonstrate awareness of real-world trade-offs: mention that tries are ideal for prefix-based searches but can be memory-heavy, and suggest alternatives like ternary search trees or compressed tries when appropriate.

1. Clarify Requirements

Ask about the expected operations (insert, search, delete, prefix search), character set (lowercase, Unicode), and any constraints on memory or performance.

2. Design the Node Structure

Define a TrieNode class with a children map/array and a boolean flag to mark the end of a word. Consider using a hash map for flexibility or an array for fixed alphabets.

3. Implement Core Operations

Write methods for insert, search, and startsWith. For insert, traverse or create nodes for each character; for search, traverse and check the end-of-word flag; for startsWith, traverse and return true if all characters exist.

4. Analyze Complexity and Optimize

Discuss time complexity O(m) for operations where m is word length, and space complexity O(total characters * alphabet size). Mention optimizations like using a compressed trie or limiting children.

5. Test and Edge Cases

Walk through examples, including empty strings, overlapping words, and deletion if required. Highlight how the trie handles these cases.

Key Points to Mention

  • Time complexity: O(m) for insert, search, and startsWith, where m is the length of the word.
  • Space complexity: O(N * M * A) where N is number of words, M is average length, and A is alphabet size; can be optimized with maps.
  • Use cases: autocomplete, spell check, IP routing, and prefix-based searches.
  • Node structure: children (array or hash map) and isEndOfWord boolean.
  • Deletion: optional but can be implemented by recursively removing nodes that are no longer needed.
  • Trade-offs: tries offer fast prefix searches but can be memory-intensive; alternatives include hash maps for exact matches or ternary search trees.

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