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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

1. In NLP tasks, transformer models perform well in multiple tasks due to their self-attention mechanism and parallel computing capability. Which of the following statements about transformer models are true?

A) Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
B) Multi-head attention is the core component of a transformer model. It computes multiple attention heads in parallel to capture semantic information in different subspaces.
C) A transformer model directly captures the dependency between different positions in the input sequence through the self-attention mechanism, without using the recurrent neural network (RNN) or convolutional neural network (CNN).
D) Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.

2. 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:

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) 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.
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) 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.

4. Which of the following is a learning algorithm used for Markov chains?

A) Viterbi algorithm
B) Baum-Welch algorithm
C) Forward-backward algorithm
D) Exhaustive search

5. Which of the following statements about the multi-head attention mechanism of the Transformer are true?

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

Solutions: