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MLA-C01 Practice Question: A data scientist is preparing text data for a…
A data scientist is preparing text data for a sentiment analysis model using Amazon SageMaker. Which two data preprocessing techniques are commonly used when working with text data for natural language processing? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse one-hot encoding as a preprocessing technique for raw text, when it is actually a feature engineering step applied after tokenization, and they may overlook that stop word removal is a standard preprocessing step despite its potential to remove sentiment-bearing words in certain contexts.
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
✓
Tokenization
Tokenization is correct because it is a fundamental preprocessing step that splits raw text into smaller units (tokens), such as words or subwords, which are necessary for converting text into a structured format that machine learning models can process. Stop word removal is correct because it filters out common words (e.g., 'the', 'and', 'is') that carry little semantic meaning, reducing noise and improving model performance in sentiment analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encoding of all words
Why it's wrong here
One-hot encoding all words creates an extremely large sparse matrix and is not recommended for NLP.
- ✗
Image resizing
Why it's wrong here
Image resizing is a technique for image data, not text.
- ✓
Tokenization
Why this is correct
Tokenization splits text into tokens (words or subwords), a fundamental step in NLP preprocessing.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique for numerical data, not typical for text preprocessing.
- ✓
Stop word removal
Why this is correct
Removing common stop words helps reduce noise and improve model performance.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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