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

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

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

2. The U-Net uses an upsampling mechanism and has a fully-connected layer.

A) FALSE
B) TRUE

3. In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)

4. The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.

A) FALSE
B) TRUE

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

Solutions: