1Z0-1127-25 Exam Dumps | How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural

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Oracle.1Z0-1127-25.v2025-06-30.q38
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Question 20/38

How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

B. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

C. Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

D. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

Correct Answer: C

Comprehensive and Detailed In-Depth Explanation=
Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague-both measure similarity, not distinct content vs. topicality. Option B is false-both address semantics, not syntax. Option D is incorrect-neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.
OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

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