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To pass the Huawei H13-311_V3.5 (HCIA-AI V3.5) Certification Exam, individuals must have a strong understanding of AI concepts and technologies. They must also possess the skills to design and develop AI applications and solutions. H13-311_V3.5 Exam is designed to test an individual's ability to apply AI concepts to real-world scenarios and solve complex problems using AI technologies.

Huawei H13-311_V3.5 (HCIA-AI V3.5) Certification Exam is a professional certification exam that focuses on the field of artificial intelligence. H13-311_V3.5 exam is designed to test your knowledge and skills in the field of AI, including the ability to design and implement AI solutions, as well as to perform AI-related tasks such as data analysis, machine learning, and natural language processing. The Huawei H13-311_V3.5 certification is ideal for professionals who are looking to enhance their career opportunities in the field of AI and machine learning.

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Huawei HCIA-AI V3.5 Sample Questions (Q348-Q353):

NEW QUESTION # 348
Which of the following items are included in the results returned when the face search service is successfully called?

A. Searched face similarity
B. Searched face number
C. Searched faces id
D. Searched face position

Answer: A,C,D

NEW QUESTION # 349
X, Y are random variables, C is a constant, the nature of the difference in the following options which is wrong? (Multiple choice)

A. D(X+ Y)=D(X)+D(Y)
B. D(CX)=C'C'D()()
C. D(C)=O
D. D(XY)=D(X)D{Y)

Answer: A,D

NEW QUESTION # 350
In the process of training the neural network, our goal is to keep the loss function reduced. Which of the following methods do we usually use to minimize the loss function?

A. Gradient descent
B. Dropout
C. Cross-validation
D. Regularization

Answer: A

NEW QUESTION # 351
Which of the following are common gradient descent methods?

A. Mini-batch gradient descent (MBGD)
B. Multi-dimensional gradient descent (MDGD)
C. Batch gradient descent (BGD)
D. Stochastic gradient descent (SGD)

Answer: A,C,D

Explanation:
The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:
Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.
Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent.
Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.
Multi-dimensional gradient descent is not a recognized method in AI or machine learning.

NEW QUESTION # 352
What model is not a cyclic neural network?

A. GRU
B. RNN
C. LSTM
D. GBOT

Answer: D

NEW QUESTION # 353
......

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Practice H13-311_V3.5 Engine & Huawei H13-311_V3.5 Latest Test Sample: HCIA-AI V3.5 Pass for Sure

H13-311_V3.5 Latest Test Sample - Test H13-311_V3.5 Pattern

Huawei HCIA-AI V3.5 Sample Questions (Q348-Q353):

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