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

1. The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)

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.)

3. Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.

A) TRUE
B) FALSE

4. 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) 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.
B) Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
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) Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.

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

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

Solutions: