MLA-C01 Data Preparation for Machine Learning Practice Question

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

⚠ Common exam trap

Many candidates choose TF-IDF (Option B) because it is a common text preprocessing technique, but they overlook the explicit requirement to 'preserve semantic meaning,' which only dense embeddings can achieve.

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

✓

Word embeddings (e.g., Word2Vec or GloVe)

Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

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 each word
Why it's wrong here
One-hot encoding creates sparse vectors without semantic similarity.
✗
Bag-of-words with TF-IDF
Why it's wrong here
TF-IDF captures term frequency but not semantic meaning.
✗
Hashing vectorizer
Why it's wrong here
Hashing vectorizer reduces dimensionality but loses semantic information.
✓
Word embeddings (e.g., Word2Vec or GloVe)
Why this is correct
Word embeddings represent words in dense vector spaces that preserve semantic relationships.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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