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This question evaluates understanding of scalable word-count processing, including tokenization and normalization rules, streaming or external-memory techniques for inputs larger than RAM, and efficient top‑K frequency computation.

  • Medium
  • Adobe
  • Data Manipulation (SQL/Python)
  • Software Engineer

Implement scalable word count locally

Company: Adobe

Role: Software Engineer

Category: Data Manipulation (SQL/Python)

Difficulty: Medium

Interview Round: Technical Screen

Write a function that reads a very large text file and outputs the frequency of each word. Define your tokenization and normalization rules (case folding, punctuation, Unicode handling), and explain how to process inputs larger than RAM using streaming, chunking, or external sorting. Discuss producing the top‑K most frequent words efficiently and analyze time and space complexity.

Quick Answer: This question evaluates understanding of scalable word-count processing, including tokenization and normalization rules, streaming or external-memory techniques for inputs larger than RAM, and efficient top‑K frequency computation.

Last updated: Mar 29, 2026

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