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

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

3. In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?

A) Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.
B) The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
C) Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
D) Hue indicates the basic color attributes, such as red, green, and blue.

4. In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.

A) TRUE
B) FALSE

5. Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A) Color image input is multi-channel, whereas text input is single-channel.
B) For CNN, there is no difference in handling text or image tasks.
C) CNNs are suitable for image tasks, but they perform poorly in text tasks.
D) When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.

Solutions: