H13-321_V2.5 VCE Exam Download

The convolutional layer in CNNs is optimized for spatial feature extraction:

Local connectivity(A) reduces computation and memory usage.

Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.

Stride control(C) allows adjusting the output resolution and computational cost.

Sliding kernel operation(D) extracts local patterns without manual feature definition.

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

"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers."

The HSV model separates chromatic content (Hue, Saturation) from brightness (Value):

H (Hue):Defines the type of color (e.g., red, blue).

S (Saturation):Measures vividness — low S means muted colors, high S means vivid colors.

V (Value):Controls brightness — increasing V brightens the image.Contrary to option D, HSV aligns more closely with human perception than RGB, making itsuitablefor segmentation and color-based analysis.

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

"HSV separates hue, saturation, and brightness, making it closer to human vision perception and suitable for color-based image analysis."

TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.

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

"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed-forward networks for sequence modeling."

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."