Huawei H13-321_V2.5 問題集

質問 1：
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
A. FALSE
B. TRUE
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
A. FALSE
B. TRUE
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
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 slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
B. 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.
C. 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.
D. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
正解：A,B,C,D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
B. The multi-head attention mechanism captures information about different subspaces within a sequence.
C. The concatenated output is fed directly into the multi-headed attention mechanism.
D. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
正解：B,D

質問 5：
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:
正解：
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

質問 6：
The jieba ------() method can be used for word segmentation.
正解：
cut
Explanation:
In Python'sjiebalibrary, the cut() method is used for Chinese word segmentation. It splits a given sentence into individual words based on probabilistic models and a dictionary. The method supports both precise mode and full mode, with precise mode being the default for balanced accuracy and completeness.
Exact Extract from HCIP-AI EI Developer V2.5:
"The jieba.cut() method segments Chinese text into words, supporting multiple modes for different application needs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Chinese Word Segmentation Tools