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Huawei H13-321_V2.5 Practice Test Questions Answers

Exam Code: H13-321_V2.5 (Updated 60 Q&As with Explanation)
Exam Name: HCIP - AI EI Developer V2.5 Exam
Last Update: 28-Aug-2025
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Questions Include:

  • Single Choice: 31 Q&A's
  • Multiple Choice: 20 Q&A's
  • Fill in the Blanks: 9 Q&A's

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    Huawei H13-321_V2.5 HCIP-AI EI Developer Practice Exam Questions and Answers

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    H13-321_V2.5 Questions and Answers

    Question # 1

    In the deep neural network (DNN)–hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.

    A.

    TRUE

    B.

    FALSE

    Question # 2

    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

    Question # 3

    Which of the following is not an algorithm for training word vectors?

    A.

    TextCNN

    B.

    BERT

    C.

    FastText

    D.

    Word2Vec

    Question # 4

    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.

    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.

    Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.

    Question # 5

    The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.

    A.

    TRUE

    B.

    FALSE

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