Question 794 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a text classification model using a bag-of-words approach. The dataset contains 1 million documents and 100,000 unique words. The resulting feature matrix is very sparse. Which technique should the data scientist use to reduce the dimensionality of the feature space?
⚠ Common exam trap
Candidates often confuse TF-IDF (a reweighting technique) with dimensionality reduction, or assume PCA can be directly applied to sparse text matrices without considering computational cost and loss of interpretability.
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
✓
Use word embeddings to represent documents
Word embeddings (e.g., Word2Vec, GloVe) map words to dense, low-dimensional vectors that capture semantic relationships, effectively reducing the 100,000-dimensional sparse bag-of-words feature space to a much smaller dense representation (e.g., 100–300 dimensions). This directly addresses the sparsity and high dimensionality of the term-document matrix while preserving meaningful word context.
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 TF-IDF transformation
Why it's wrong here
TF-IDF weights terms but does not reduce the number of features.
- ✓
Use word embeddings to represent documents
Why this is correct
Word embeddings create dense low-dimensional vectors, reducing sparsity and dimensionality.
- ✗
Remove stop words from the vocabulary
Why it's wrong here
Removing stop words reduces dimensions but may not be sufficient and can remove meaningful words.
- ✗
Apply Principal Component Analysis (PCA) to the term-document matrix
Why it's wrong here
PCA can reduce dimensions but is less effective for sparse text data than embeddings.
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Last reviewed: Jun 24, 2026
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