MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset containing text reviews. To understand the most common words, the data scientist generates a word cloud. Which preprocessing step is most important to ensure the word cloud reflects meaningful content?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Stop word removal
Removing stop words (common words like 'the', 'and') ensures that the word cloud highlights meaningful content. Stemming (C) may not be necessary for a word cloud. Tokenization (D) is fundamental but not the most critical for meaningfulness. POS tagging (B) is overkill.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Stop word removal
Why this is correct
Stop word removal eliminates common, uninformative words.
- ✗
Part-of-speech tagging
Why it's wrong here
POS tagging is advanced and not typically used for word clouds.
- ✗
Stemming
Why it's wrong here
Stemming reduces words to root form but may not remove common words.
- ✗
Tokenization
Why it's wrong here
Tokenization is necessary but does not filter out noise.
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