Question 312 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing text data for natural language processing (NLP). The corpus contains many rare words and typos. To reduce dimensionality and improve generalization, they decide to apply stemming and remove stop words. However, after training, the model performs poorly on domain-specific terms. What is the most likely cause?
⚠ Common exam trap
AWS often tests the misconception that lemmatization is always superior to stemming, but the trap here is that the root cause is the inappropriate application of both preprocessing techniques to domain-specific text, not the choice between stemming and lemmatization.
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
✓
Both stemming and stop word removal are inappropriate for the domain
Both stemming and stop word removal are inappropriate for this domain. Stemming aggressively reduces words to their root forms, which can conflate distinct domain-specific terms (e.g., 'therapy' and 'therapist' both stem to 'therap'), losing critical semantic nuance. Stop word removal can discard words that carry domain-specific meaning (e.g., 'not' in medical negation or 'up' in 'tune-up' for maintenance), leading to poor generalization on specialized vocabulary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The corpus should be lemmatized instead
Why it's wrong here
Lemmatization may help but does not address the stop word issue.
- ✓
Both stemming and stop word removal are inappropriate for the domain
Why this is correct
In specialized domains, stemming can distort meaning and stop words can carry essential context.
- ✗
Stemming is too aggressive for the domain
Why it's wrong here
While stemming could be aggressive, removing stop words could also be harmful; the combination is the issue.
- ✗
Stop word removal removed important context words
Why it's wrong here
Stop word removal might remove domain-specific context, but stemming is also problematic; both together are likely the cause.
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Last reviewed: Jun 30, 2026
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