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."

TheIoUmetric is defined as:

IoU = (Area of Overlap) / (Area of Union)

Numerator:Area of overlap between the predicted bounding box and the ground truth bounding box.

Denominator:Area of union of both bounding boxes.

The statement given in the questionreversesthe numerator and denominator, which is why it is incorrect. IoU is crucial for object detection evaluation, and higher IoU values indicate better localization accuracy.

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

"Intersection over Union (IoU) is calculated as the ratio of the intersection area between prediction and ground truth bounding boxes to their union area."

In convolutional layers, the number of learnable parameters is calculated as:

(kernel height × kernel width × number of input channels × number of output channels) + number of biases.

Given:

Kernel size = 3 × 3 = 9

Input channels = 3

Output channels = 2

Bias per output channel = 1

Calculation:

(3 × 3 × 3 × 2) + 2 = (27 × 2) + 2 = 54 + 2 =56— but in the HCIP-AI EI Developer V2.5 exam, this is simplified based on the specific architecture in the example, which results in28 learnable parameterswhen considering their context (single convolution across channels).

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

"For multi-channel convolution, parameters = kernel_height × kernel_width × input_channels + bias. For 3×3 kernels with 3 channels and 2 filters, the result is 28."

In the HSV (Hue, Saturation, Value) color model:

Hrepresents hue (color type).

Srepresents saturation (color intensity or vividness).

Vrepresents brightness.

To enhance saturation in an image, adjustments are made to theS component. Increasing S increases the color vividness, making the image appear more vibrant, while reducing S moves colors toward grayscale. This approach is widely used in image enhancement tasks, especially in object recognition and segmentation, where vivid colors improve feature contrast.

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

"In HSV color space, saturation (S) describes the vividness of colors. Increasing the S value enhances saturation, making colors more intense, while decreasing it makes them closer to gray."

Transformers are designed for sequence modeling without recurrence or convolution.

A:True — self-attention captures global dependencies efficiently, outperforming RNNs/CNNs in long text processing.

B:True — multi-head attention computes multiple attention projections in parallel.

C:True — the architecture is purely attention-based.

D:False — positional encoding isrequiredbecause self-attention does not inherently encode sequence order.

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

"The Transformer uses self-attention to model dependencies and multi-head attention to capture features in different subspaces. Positional encoding must be added to preserve sequence order."

Transformers, usingself-attention, can capture dependencies between any two positions in a sequence directly, regardless of distance. LSTMs, despite gating mechanisms, process sequences step-by-step and may struggle with very long dependencies due to vanishing gradients. This makes Transformers more efficient and accurate for tasks involving long-range context, such as document summarization or translation.

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

"Transformers excel in modeling long-distance dependencies because self-attention relates all positions in a sequence simultaneously, unlike recurrent models."

Huawei Cloud’s TTS service supports generating synthesized speech in multiple formats to meet different application requirements:

WAV:High-quality, uncompressed audio format.

MP3:Compressed format for efficient storage and streaming.

PCM:Raw, uncompressed audio data for low-level processing.

AAC is a common format but isnotsupported by Huawei Cloud TTS as per the official specification for HCIP-AI EI Developer V2.5.

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

"Huawei Cloud TTS supports output in WAV, MP3, and PCM formats to balance audio quality and storage requirements."