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Pass H13-321_V2.5 Guide - H13-321_V2.5 Valid Exam Guide

The HCIP-AI-EI Developer V2.5 (H13-321_V2.5) exam dumps are real and updated H13-321_V2.5 exam questions that are verified by subject matter experts. They work closely and check all HCIP-AI-EI Developer V2.5 (H13-321_V2.5) exam dumps one by one. They maintain and ensure the top standard of HCIP-AI-EI Developer V2.5 (H13-321_V2.5) exam questions all the time.

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q55-Q60):

NEW QUESTION # 55
In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)

A. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
B. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
C. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
D. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.

Answer: A,B,C,D

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Neural Networks

NEW QUESTION # 56
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. FALSE
B. TRUE

Answer: B

Explanation:
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models

NEW QUESTION # 57
Which of the following is not an algorithm for training word vectors?

A. TextCNN
B. FastText
C. Word2Vec
D. BERT

Answer: A

Explanation:
* Word2VecandFastTextare neural network-based algorithms designed for generating dense vector representations of words.
* BERTis a transformer-based language model that also generates contextualized word embeddings.
* TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.
Exact Extract from HCIP-AI EI Developer V2.5:
"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation

NEW QUESTION # 58
A text classification task has only one final output, while a sequence labeling task has an output in each input position.

A. FALSE
B. TRUE

Answer: B

Explanation:
In NLP:
* Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.
* Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Task Categories

NEW QUESTION # 59
The image saturation can be enhanced by processing the ________ component of the HSV color space. (Enter H, S, or V.)

Answer:

Explanation:
S
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Processing Basics

NEW QUESTION # 60
......

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