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

2. Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?

A) In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
B) Residual connections primarily add depth to the model but do not aid in gradient propagation.
C) Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
D) Layer normalization accelerates model convergence and does not affect model stability.

3. Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.

A) FALSE
B) TRUE

4. Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?

A) It can process high-resolution images to enhance classification accuracy.
B) The self-attention mechanism is used to capture global features of images, improving classification accuracy.
C) It achieves fast convergence without using pre-trained models.
D) It can handle small datasets with minimal labeling required.

5. Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A) When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
B) For CNN, there is no difference in handling text or image tasks.
C) CNNs are suitable for image tasks, but they perform poorly in text tasks.
D) Color image input is multi-channel, whereas text input is single-channel.

Solutions: