MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is cleaning a text dataset for natural language processing. The raw data contains HTML tags, URLs, and special characters. Which THREE steps should be taken to preprocess the text data? (Choose 3.)
⚠ Common exam trap
AWS often tests the distinction between preprocessing steps that clean raw data (like removing HTML tags and normalizing case) versus later feature engineering steps (like encoding or stop word removal), causing candidates to mistakenly select stop word removal as a cleaning step when it is actually a filtering step applied after tokenization.
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
✓
Convert all text to lowercase
Converting all text to lowercase (Option A) is a standard text normalization step in NLP preprocessing. It reduces the vocabulary size by treating words like 'Apple' and 'apple' as the same token, which helps downstream models avoid treating case variations as distinct features. This is typically done early in the pipeline before tokenization or vectorization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Convert all text to lowercase
Why this is correct
Lowercasing standardizes text and reduces vocabulary size.
- ✗
Encode the text using one-hot encoding
Why it's wrong here
One-hot encoding is a feature extraction step, not a cleaning step.
- ✓
Remove HTML tags using a regular expression
Why this is correct
HTML tags are noise and should be removed before analysis.
- ✓
Perform stemming or lemmatization
Why this is correct
Stemming/lemmatization reduces words to root forms, aiding generalization.
- ✗
Remove stop words
Why it's wrong here
Stop word removal is optional and may not be appropriate for all NLP tasks (e.g., sentiment analysis).
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