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

1. 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

2. Which of the following methods are useful when tackling overfitting?

A) Data augmentation
B) Using parameter norm penalties
C) Using more complex models
D) Using dropout during model training

3. The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.

A) FALSE
B) TRUE

4. The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.

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

Solutions: