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[Hardware] New H13-321_V2.5 Braindumps Pdf & Exam H13-321_V2.5 Score

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【Hardware】 New H13-321_V2.5 Braindumps Pdf & Exam H13-321_V2.5 Score

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q44-Q49):NEW QUESTION # 44
When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using:
Answer:
Explanation:
python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods

NEW QUESTION # 45
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. Object detection
  • B. Video analytics
  • C. Speech recognition
  • D. Image classification
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 # 46
In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
  • A. TRUE
  • B. FALSE
Answer: A
Explanation:
In hybridDNN-HMMspeech recognition:
* TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.
* TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.
This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.
Exact Extract from HCIP-AI EI Developer V2.5:
"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models

NEW QUESTION # 47
Which of the following methods are useful when tackling overfitting?
  • A. Using dropout during model training
  • B. Using parameter norm penalties
  • C. Using more complex models
  • D. Data augmentation
Answer: A,B,D
Explanation:
To address overfitting, HCIP-AI EI Developer V2.5 outlines multiple strategies:
* Dropout:A regularization method that randomly ignores certain neurons during training, preventing reliance on specific paths and improving generalization.
* Data augmentation:Expands the training dataset by applying transformations (rotation, scaling, flipping) to existing data, increasing diversity and reducing overfitting risk.
* Parameter norm penalties:Techniques such as L1 and L2 regularization add a penalty to large parameter values, discouraging overly complex models.
Using amore complex model(Option B) is the opposite of what is recommended, as it generally increases the risk of overfitting.
Exact Extract from HCIP-AI EI Developer V2.5:
"Common overfitting mitigation techniques include data augmentation to expand datasets, dropout to randomly deactivate neurons during training, and applying regularization penalties to constrain model complexity." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Preventing Overfitting

NEW QUESTION # 48
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. CNNs are suitable for image tasks, but they perform poorly in text tasks.
  • C. Color image input is multi-channel, whereas text input is single-channel.
  • 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: C,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 # 49
......
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