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Huawei HCIA-AI V3.5 Sample Questions (Q304-Q309):

NEW QUESTION # 304
Feedforward neural network is a simple neural network, each neuron is arranged hierarchically, it is currently one of the most widely used and fastest growing artificial neural networks. The following statement about feedforward neural networks is correct:

A. Connecting neurons in the same layer
B. Its input node has computing power
C. Information only along-Pass through
D. Neurons with computing power are connected to the upper and lower layers

Answer: C

NEW QUESTION # 305
HUAWEI HiAI Engine Can easily combine multiple AI Ability and App integrated.

A. TRUE
B. FALSE

Answer: A

NEW QUESTION # 306
All kernels of the same convolutional layer in a convolutional neural network share a weight.

A. FALSE
B. TRUE

Answer: A

Explanation:
In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.
However, the same kernel's weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.
Thus, the statement that all kernels share weights is false.
HCIA AI
Reference:
Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.

NEW QUESTION # 307
Python script execution mode includes interactive mode and script mode

A. True
B. False

Answer: A

NEW QUESTION # 308
L1 with L2 Regularization is a method commonly used in traditional machine learning to reduce generalization errors. The following is about the two.
The right way is:

A. L1 with L2 Regularization cannot be used for feature selection
B. L2 Regularization can do feature selection
C. L1 with L2 Regularization can be used for feature selection
D. L1 Regularization can do feature selection

Answer: D

NEW QUESTION # 309
......

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