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Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?
Correct Answer: B
# Edit distance (also known as Levenshtein distance) measures how many single-character edits (insertions, deletions, or substitutions) are needed to transform one string into another. It's a common metric for assessing string similarity, especially in natural language processing (NLP) tasks. Why the other options are incorrect: * A: Word clouds visualize word frequency, not similarity. * C: String indexing is a method for referencing string positions, not comparison. * D: k-NN is a classification algorithm, not a string similarity measure. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Edit distance is a key similarity metric in text comparison tasks, particularly in cleaning or matching string records." -
