Free PDF Quiz Huawei - H13-311_V3.5 - Marvelous Dumps HCIA-AI V3.5 PDF
Free PDF Quiz Huawei - H13-311_V3.5 - Marvelous Dumps HCIA-AI V3.5 PDF
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Huawei H13-311_V3.5 Discount Code & Certification H13-311_V3.5 Dumps
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Huawei H13-311_V3.5 (HCIA-AI V3.5) Certification Exam is an industry-recognized certification that validates expertise in artificial intelligence. HCIA-AI V3.5 certification is designed for professionals who want to enhance their knowledge and skills in the field of AI and become an expert in this domain. H13-311_V3.5 Exam covers various topics such as machine learning, deep learning, natural language processing, and computer vision.
Huawei HCIA-AI V3.5 Sample Questions (Q126-Q131):
NEW QUESTION # 126
The activation function plays an important role in the neural network model learning and understanding of very complex problems. The following statement about the activation function is correct.
- A. Activation functions are non-linear functions
- B. Most of the activation functions are nonlinear functions, and a few are linear functions
- C. Activation functions are linear functions
- D. The activation function is partly a nonlinear function, partly a linear function
Answer: A
NEW QUESTION # 127
Which of the following statements about datasets are true?
- A. In machine learning, a dataset is generally divided into a training set, validation set, and test set.
- B. When it comes to the machine learning process, the validation set and the test set are essentially the same.
- C. A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
- D. Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.
Answer: A,C,D
Explanation:
In machine learning:
The testing set is a dataset used after training to evaluate the model's performance and generalization ability. Each sample in this set is called a test sample.
A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.
A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model's final performance).
The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.
NEW QUESTION # 128
In the face search service, if we want to delete a certain face set, we can use this code:
firs_cliet.get_v2().get_face_set_service().delete_face_set("**"), among them "**" is to fill in the actual face set name.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 129
Huawei firmly believes that the value of Al is ultimately reflected in solving the actual business problems of the enterprise. Therefore, Huawei is firmly committed to the development of Al technology, focusing on domain solutions, focusing on basic technologies and enabling platforms, and working with industry practice leaders to develop industry solutions
- A. True
- B. False
Answer: A
NEW QUESTION # 130
Which of the following statements is false about feedforward neural networks?
- A. Each neuron is connected to all neurons at the previous layer.
- B. A unidirectional multi-layer structure is adopted. Each layer includes several neurons, and those in the same layer are not connected to each other. Only unidirectional inter-layer information transmission is supported.
- C. Nodes at each hidden layer represent neurons that provide the computing function.
- D. Input nodes do not provide the computing function and are used to represent only the element values of an input vector.
Answer: A
Explanation:
This statement is false because not all feedforward neural networks follow this architecture. While fully-connected layers do have this type of connectivity (where each neuron is connected to all neurons in the previous layer), feedforward networks can include layers like convolutional layers, where not every neuron is connected to all previous neurons. Convolutional layers, common in convolutional neural networks (CNNs), only connect to a local region of the input, preserving spatial information.
NEW QUESTION # 131
......
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