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

2. In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)

A) In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
B) A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
C) The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
D) The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.

3. If OpenCV is used to read an image and save it to variable "img" during image preprocessing, (h, w) = img.
shape[:2] can be used to obtain the image size.

A) FALSE
B) TRUE

4. Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?

A) Image classification
B) Video analytics
C) Speech recognition
D) Object detection

5. In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)

Solutions: