Question 102 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A team is preparing text data for a natural language processing (NLP) model. They have a corpus of customer reviews. Which THREE preprocessing steps are essential to reduce noise and improve model performance?
⚠ Common exam trap
AWS often tests the distinction between preprocessing steps (cleaning) and feature engineering steps (vectorization), so the trap here is that candidates mistake TF-IDF or one-hot encoding as essential preprocessing for noise reduction when they are actually downstream representation techniques.
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
✓
Remove punctuation and special characters
Punctuation and special characters (e.g., commas, exclamation marks) introduce irrelevant noise that does not carry semantic meaning for most NLP models. Removing them reduces vocabulary size and prevents the model from treating 'hello!' and 'hello' as distinct tokens, which improves generalization and reduces overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply one-hot encoding to each word
Why it's wrong here
One-hot encoding is for categorical variables, not text preprocessing.
- ✓
Remove punctuation and special characters
Why this is correct
Removes noise that does not contribute to meaning.
- ✗
Compute TF-IDF vectors
Why it's wrong here
TF-IDF is a feature extraction step, not preprocessing.
- ✓
Perform stemming or lemmatization
Why this is correct
Reduces words to root form, reducing dimensionality.
- ✓
Convert all text to lowercase
Why this is correct
Reduces vocabulary size and treats words like 'The' and 'the' as same.
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Last reviewed: Jun 30, 2026
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