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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #130

You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam p

The correct answer is B. New problematic phrases can be identified in spam posts.. A static keyword list can only catch known spam patterns that were manually added. The primary advantage of machine learning is its ability to learn patterns from data and generalize to new, previously unseen examples. An ML model can discover new problematic phrases, evolving sl

Submitted by khalil_dz· Apr 18, 2026Problem framing

Question

You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

Options

  • APosts can be compared to the keyword list much more quickly.
  • BNew problematic phrases can be identified in spam posts.
  • CA much longer keyword list can be used to flag spam posts.
  • DSpam posts can be flagged using far fewer keywords.

How the community answered

(30 responses)
  • A
    7% (2)
  • B
    90% (27)
  • C
    3% (1)

Explanation

A static keyword list can only catch known spam patterns that were manually added. The primary advantage of machine learning is its ability to learn patterns from data and generalize to new, previously unseen examples. An ML model can discover new problematic phrases, evolving slang, and novel spam tactics that were never explicitly defined in any keyword list. The other options are incorrect: ML is not inherently faster than a hash-based keyword lookup (A), it does not require a longer keyword list (C), and reducing the keyword count is not the goal or a natural outcome (D).

Topics

#Machine Learning advantages#Rule-based vs ML#Spam detection#Problem framing

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