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New H13-321_V2.5 Test Pass4sure - H13-321_V2.5 Exam Guide

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q52-Q57):

NEW QUESTION # 52
In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)

Answer:

Explanation:
3
Explanation:
In OpenCV:
* cv2.imread(filename, 1) reads the image incolor mode.
* This loads the image as a3-channelBGR image (Blue, Green, Red).
* Other modes: 0 for grayscale, -1 for unchanged (including alpha channel).
Exact Extract from HCIP-AI EI Developer V2.5:
"When the second parameter of cv2.imread is 1, the image is read in color mode, resulting in a 3-channel BGR image." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Reading and Writing with OpenCV

NEW QUESTION # 53
Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?

A. Video analytics
B. Image classification
C. Object detection
D. Speech recognition

Answer: A,B,C,D

Explanation:
ModelArts provides an integrated environment for data labeling, model training, deployment, and management, supporting various AI application scenarios:
* Image classificationfor categorizing visual content.
* Object detectionfor locating and identifying multiple objects in images or video frames.
* Speech recognitionfor converting speech to text.
* Video analyticsfor automated video content analysis.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts supports a wide range of AI tasks including image classification, object detection, speech recognition, and intelligent video analytics." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Overview

NEW QUESTION # 54
------- 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 # 55
In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?

A. Hue indicates the basic color attributes, such as red, green, and blue.
B. The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
C. Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
D. Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.

Answer: A,C,D

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Color Spaces

NEW QUESTION # 56
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A. CNNs are suitable for image tasks, but they perform poorly in text tasks.
B. 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.
C. Color image input is multi-channel, whereas text input is single-channel.
D. For CNN, there is no difference in handling text or image tasks.

Answer: B,C

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 # 57
......

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