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A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to accomplish this task after data ingestion?
Correct Answer: A
# Embeddings (e.g., word2vec, sentence transformers) are vector representations of text that capture semantic similarity. They allow comparison of conceptual meaning between sentences in a high-dimensional space, which is essential for tasks like semantic similarity or clustering. Why the other options are incorrect: * B: Extrapolation predicts values beyond a dataset's range - not relevant here. * C: Sampling reduces data volume but doesn't aid in similarity analysis. * D: One-hot encoding captures presence of words but lacks semantic understanding. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Embeddings transform text into numeric vectors, enabling similarity computation and semantic analysis." -
