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

1. 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.

2. 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) 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.
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) 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".

3. Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)

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

5. In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)

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