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

1. 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) Layer normalization accelerates model convergence and does not affect model stability.
D) Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.

2. Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A) TRUE
B) FALSE

3. Overfitting is a condition where a model is overly simple and excessive generalization errors occur.

A) TRUE
B) FALSE

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

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

5. When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.

A) TRUE
B) FALSE

Solutions: