The correct answer is D. Word Embeddings. Here's why:

• A. Tokenization: Splits text into tokens (words, phrases) but doesn't convert them into numerical vectors.
• B. Text Encoding: Converts text characters into numerical codes (e.g., ASCII) without capturing semantic meaning.
• C. Feature Extraction: A broad term for techniques to extract features from data, not specifically for text-to-vector conversion.
• D. Word Embeddings (CORRECT): Maps words to numerical vectors in a way that similar words have similar vectors, capturing semantic relationships. Databricks MLlib supports integration with pre-trained models like Word2Vec or GloVe for this purpose.

Using Word Embeddings in Databricks MLlib:

1. Load Text Data: Import your text data into a DataFrame.
2. Preprocessing: Tokenize and clean the text (e.g., remove stop words).
3. Load Pre-trained Word Embeddings: Utilize models from sources like Gensim or spaCy.
4. Convert Words to Vectors: Apply the model to transform words into numerical vectors.
5. Machine Learning Model: Use these vectors as features in your ML model.

This approach effectively converts textual data into numerical features, enhancing your machine learning models' performance.