Exam H13-321_V2.5 Topic 1 Question 38 Discussion

Actual exam question for Huawei's H13-321_V2.5 exam
Question #: 38
Topic #: 1
The natural language processing field usually uses distributed semantic representation to represent words.
Each word is no longer a completely orthogonal 0-1 vector, but a point in a multi-dimensional real number space, which is specifically represented as a real number vector.

Suggested Answer: A Vote an answer

Traditional word representations like one-hot vectors are sparse and orthogonal, failing to capture semantic similarities.Distributed semantic representations(word embeddings) map words to dense, continuous vectors in a multi-dimensional space where similar words have similar vector representations. This approach enables better generalization and semantic reasoning in NLP tasks.
Exact Extract from HCIP-AI EI Developer V2.5:
"Distributed semantic representation maps words to dense real-valued vectors in continuous space, allowing semantic similarity to be captured in vector geometry." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation

by Christopher at Jul 10, 2026, 01:04 PM

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