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Huawei H13-321_V2.5 Exam Syllabus Topics:

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) TRUE
B) FALSE

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. Which of the following are the impacts of the development of large models?

A) Large models will completely replace small and domain-specific models
B) Model pre-training costs will be reduced
C) Data privacy and security issues will be exacerbated
D) The accuracy and efficiency of natural language processing tasks will improve

5. The attention mechanism in foundation model architectures allows the model to focus on specific parts of the input data. Which of the following steps are key components of a standard attention mechanism?

A) Calculate the dot product similarity between the query and key vectors to obtain attention scores.
B) Compute the weighted sum of the value vectors using the attention weights.
C) Apply a non-linear mapping to the result obtained after the weighted summation.
D) Normalize the attention scores to obtain attention weights.

Solutions: