Huawei H13-321_V2.5 Dumps Questions Answers

The standardattention mechanisminvolves:

Computing attention scores via the dot product of query and key vectors (A).

Applying a normalization function (typically softmax) to obtain attention weights (D).

Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step — non-linear mappings are not applied after the weighted sum in the basic attention formula.

Exact Extract from HCIP-AI EI Developer V2.5:

"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors."

A:Incorrect — description given matches semantic analysis, not syntactic analysis.

B:Incorrect — description given matches syntactic analysis, not semantic analysis.

C:Correct — speech analysis focuses on phoneme recognition and word identification.

D:Correct — lexical analysis identifies lexemes and retrieves their linguistic details.

E:Correct — pragmatic analysis studies language use in context and environment.

Exact Extract from HCIP-AI EI Developer V2.5:

"Natural language understanding involves lexical, syntactic, semantic, speech, and pragmatic analyses, each focusing on different layers of language processing."

In CNN usage:

A:True — color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.

B:True — in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.

C:False — there are clear differences in handling between text and image data.

D:False — CNNs can perform very well in text classification when used appropriately.

Exact Extract from HCIP-AI EI Developer V2.5:

"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count."