Text Preprocessing DATA-SCIENCE-OPTIMIZE Practice Questions (Page 9)

Questions 41–44

1. 41foundation · easy

   What is the key difference between lemmatization and stemming?

   • ALemmatization splits text into tokens
   • BLemmatization returns a valid dictionary word, while stemming may not
   • CLemmatization only removes stop words
   • DLemmatization is rule-based and faster than stemming
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2. 42foundation · easy

   Which of the following is an example of text normalization?

   • AReplacing multiple spaces with a single space
   • BConverting text to a vector representation
   • CSplitting text into sentences
   • DRemoving stop words
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4. 43application · medium

   A web-scraping pipeline collects product descriptions that include HTML tags, embedded URLs, and special characters like '&'. The team needs clean text for a recommendation model. What is the correct order of operations?

   • AStrip HTML tags, decode HTML entities, remove URLs, then normalize whitespace.
   • BNormalize whitespace first, then strip tags, then decode entities.
   • CRemove URLs first, then strip HTML tags, then decode entities.
   • DDecode HTML entities first, then remove URLs, then strip tags.
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5. 44foundation · easy

   What is a typical way to represent an unknown token in a vocabulary?

   • AConverting it to a number
   • BRemoving it from the text
   • CUsing a special token like <UNK>
   • DAssigning it the index 0
   Reveal Solution Discussion Select an answer first