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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

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

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

2. The mAP evaluation metric in object detection combines accuracy and recall.

A) TRUE
B) FALSE

3. Which of the following is not an acoustic feature of speech?

A) Semantics
B) Duration
C) Frequency
D) Amplitude

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

5. Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)

Solutions: