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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

1. What type of task is viewed when using the Seq2Seq model in speech recognition?

A) Clustering task
B) Regression task
C) Classification task
D) Dimensionality reduction task

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

3. Among image preprocessing techniques, gamma correction is a common non-linear brightness adjustment method. Which of the following statements are true about the application and features of gamma correction?

A) Gamma correction applies only to grayscale images and does not apply to color images.
B) When # > 1, the input low grayscale range is compressed, and the high grayscale range is stretched, enhancing the bright areas while compressing the dark areas.
C) When # < 1, the input high grayscale range is compressed, and the low grayscale range is stretched, enhancing the dark areas while compressing the bright areas.
D) Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.

4. -------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)

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

Solutions: