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Pass Guaranteed 2025 Huawei H13-321_V2.5: HCIP-AI-EI Developer V2.5 Perfect Knowledge Points

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q54-Q59):

NEW QUESTION # 54
Which audio file formats can Huawei Cloud text-to-speech (TTS) generate?

A. PCM
B. AAC
C. MP3
D. WAV

Answer: A,C,D

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Text-to-Speech Service

NEW QUESTION # 55
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A. For CNN, there is no difference in handling text or image tasks.
B. Color image input is multi-channel, whereas text input is single-channel.
C. CNNs are suitable for image tasks, but they perform poorly in text tasks.
D. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.

Answer: B,D

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP

NEW QUESTION # 56
The U-Net uses an upsampling mechanism and has a fully-connected layer.

A. FALSE
B. TRUE

Answer: A

Explanation:
U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.
Exact Extract from HCIP-AI EI Developer V2.5:
"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Semantic Segmentation Networks

NEW QUESTION # 57
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.

A. TRUE
B. FALSE

Answer: A

Explanation:
In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.
Exact Extract from HCIP-AI EI Developer V2.5:
"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training and Evaluation

NEW QUESTION # 58
------- is a model that uses a convolutional neural network (CNN) to classify texts.

Answer:

Explanation:
Text CNN
Explanation:
Text CNN applies convolutional layers directly to text data represented as word embeddings. By using multiple kernel sizes, Text CNN captures features from n-grams of varying lengths. These features are pooled and passed to fully connected layers for classification tasks such as sentiment analysis or spam detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text CNN applies convolution and pooling over word embeddings to extract local features for text classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN Applications in NLP

NEW QUESTION # 59
......

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