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

1. If a scanned document is not properly placed, and the text is tilted, it is difficult to recognize the characters in the document. Which of the following techniques can be used for correction in this case?

A) Grayscale transformation
B) Affine transformation
C) Perspective transformation
D) Rotational transformation

2. The mAP evaluation metric in object detection combines accuracy and recall.

A) TRUE
B) FALSE

3. 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.

4. Overfitting is a condition where a model is overly simple and excessive generalization errors occur.

A) TRUE
B) FALSE

5. 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 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.
C) 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.
D) In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.

Solutions: