1. **Word2Vec Embedding**:
   - Word2Vec typically creates embeddings in spaces ranging from 100 to 300 dimensions. Let's assume we are using a 300-dimensional model.
   - Each word in the phrase "And it came to pass..." would be converted into a 300-dimensional vector.

2. **Representation of Each Word**:
   - The phrase has 5 words, so we would have 5 vectors.
   - Each dimension in the vector is usually a 32-bit floating-point number.

3. **Memory Calculation**:
   - Each 32-bit float requires 4 bytes of memory.
   - A 300-dimensional vector would thus require \( 300 \times 4 \) bytes = 1200 bytes.
   - For 5 words, the total memory would be \( 5 \times 1200 \) bytes = 6000 bytes (or 6 kilobytes).

So, in this hypothetical scenario, representing the phrase "And it came to pass..." using a 300-dimensional Word2Vec model would require approximately 6 kilobytes of memory.