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Huawei H13-321_V2.5 Exam Syllabus Topics:

Huawei HCIP-AI-EI Developer V2.5 Sample Questions:

Question 1

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:

Question 2

-------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)

Question 3

Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?

A. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
B. It achieves fast convergence without using pre-trained models.
C. It can process high-resolution images to enhance classification accuracy.
D. It can handle small datasets with minimal labeling required.

Question 4

In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?

A. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
B. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
C. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
D. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.

Question 5

Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?

A. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
B. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
C. Layer normalization accelerates model convergence and does not affect model stability.
D. Residual connections primarily add depth to the model but do not aid in gradient propagation.

Solutions: