Practice Free H13-311_V3.5 Exam Online Questions
In MindSpore, the basic unit of the neural network is nn.Cell.
- A . TRUE
- B . FALSE
A
Explanation:
In MindSpore, nn.Cell is the basic unit of a neural network. It represents layers, models, and other neural network components, encapsulating the forward logic of the network. It allows users to define, organize, and manage neural network layers in MindSpore, making it a core building block in neural network construction.
Reference: Huawei HCIA-AI Certification, AI Development Framework C MindSpore Neural Network Structure.
An e-commerce company has always had problems with official website stalls, poor utilization of network resources, and difficulty in operation and maintenance.
Then which of the following can the company use EI service?
- A . Park Agent
- B . Traffic Agent
- C . Network agent
- D . Industrial Agent
According to Huawei Cloud EI Intelligent platform, which of the following solutions can be provided? (Multiple choice)
- A . Crowd statistics program
- B . Policy query scheme based on knowledge graph
- C . Vehicle identification scheme
- D . Intrusion recognition scheme
enter 32*32 Image with size 5*5 The step size of the convolution kernel is 1 Convolution calculation, output image Size is:
- A . 28*23
- B . 28*28
- C . 29*29
- D . 23*23
Huawei AI The full scenarios include public cloud, private cloud, various edge computing, IoT industry terminals, and consumer terminals and other end, edge, and cloud deployment environments.
- A . TRUE
- B . FALSE
In polynomial regression, there is a square term in the formula of the model, so it is not linear.
- A . TRUE
- B . FALSE
The commonly used loss functions in deep learning are? (Multiple Choice)
- A . L1 Loss function
- B . Mean square error loss function
- C . Cross entropy error loss function
- D . Self-declining loss function
DRAG DROP
Match the input and output of a generative adversarial network (GAN).
Explanation:
Based on the image you’ve described, here’s how the inputs and outputs of a Generative Adversarial Network (GAN) should be matched:
Gaussian white noise vector: This is typically used as an Input to the generator in GANs. The generator uses this random noise vector to produce synthetic data.
Sample data vector: This is an Input to the discriminator. The discriminator in a GAN receives either real data from the training set or fake data generated by the generator to determine its authenticity. Real sample data or generated sample data: This is the Output from the generator. The generator creates synthetic data that mimics the real training data, which is then fed into the discriminator. True or false: This is the Output from the discriminator. The discriminator outputs a judgment about whether the input data it received (either from real datasets or generated by the generator) is real (true) or fake (false).
These matches align with how GANs are designed to operate, with the generator creating data and the discriminator evaluating it.
Which of the following statements are false about softmax and logistic?
- A . In terms of probability, softmax modeling uses the polynomial distribution, whereas logistic modeling uses the binomial distribution.
- B . Multiple logistic regressions can be combined to achieve multi-class classification effects.
- C . Logistic is used for binary classification problems, whereas softmax is used for multi-class classification problems.
- D . In the multi-class classification of softmax regression, the output classes are not mutually exclusive. That is, the word "Apple" belongs to both the "fruit" and "3C" classes.
Pytorch Which company launched it first?
- A . Baidu
- B . Google
- C . Facebook
- D . Huawei