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
Option B is correct because removing punctuation and special characters eliminates non-linguistic symbols that add noise and are typically not useful features for NLP models, helping normalize the token stream. Option D is correct because stemming or lemmatization reduces inflected words to their base or dictionary form (e.g., 'running' to 'run'), decreasing vocabulary size and helping the model generalize across morphological variants. Option E is correct because converting all text to lowercase ensures that words like 'Review' and 'review' are treated as the same token, preventing spurious vocabulary duplication and improving consistency. Option A is not a noise-reduction preprocessing step; one-hot encoding is a feature representation technique that actually increases dimensionality and does not clean the text. Option C is also not a preprocessing cleaning step; TF-IDF is a numerical vectorization/weighting method applied after text has already been normalized and tokenized.
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
Removing punctuation and special characters strips non-linguistic tokens that inflate vocabulary size and dilute token frequency statistics, directly reducing noise in the customer review corpus. This normalisation step ensures the NLP model learns from meaningful word patterns rather than artefacts like commas, hashtags or emojis, satisfying the stem's noise-reduction requirement.
- ✗
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
Stemming and lemmatisation collapse inflected forms such as "running", "ran" and "runs" to a shared root, cutting vocabulary size and letting the model generalise across morphological variants instead of treating each as a distinct feature.
- ✓
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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