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H13-311_V3.5 Hot Questions - New H13-311_V3.5 Exam Duration

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Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is an essential certification for IT professionals who want to specialize in the field of AI. H13-311_V3.5 exam is designed to validate the knowledge and skills required to design and deploy AI solutions on Huawei platforms. H13-311_V3.5 exam covers a range of topics, including AI architecture, machine learning, deep learning, natural language processing, and computer vision. HCIA-AI V3.5 certification provides a competitive advantage to professionals who wish to pursue a career in AI development and deployment. It demonstrates the candidate's proficiency in designing and deploying AI solutions and validates their skills to potential employers. HCIA-AI V3.5 certification also provides an opportunity for career growth and advancement.

>> H13-311_V3.5 Hot Questions <<

New H13-311_V3.5 Exam Duration & Cost Effective H13-311_V3.5 Dumps

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Huawei HCIA-AI V3.5 Sample Questions (Q353-Q358):

NEW QUESTION # 353
Which of the following is not a face recognition technology?

A. Face Detection
B. Remake recognition
C. Face comparison
D. Face search

Answer: B

NEW QUESTION # 354
What are the steps that are not part of the operation of a Python file object?

A. read
B. delete
C. open
D. write
E. close

Answer: B

NEW QUESTION # 355
In the deep learning network, the backpropagation algorithm is used to find the optimal parameters. What rules are used in the backpropagation algorithm to obtain the derivation layer by layer?

A. Cumulative rule
B. Rule of Normalization
C. Chain rule
D. Law of reciprocity

Answer: C

NEW QUESTION # 356
Principal Component Analysis (PCA) is a statistical method. A set of variables that may be related to each other is transformed into a set of linearly related variables by orthogonal transformation The converted set of variables is called the principal component

A. False
B. True

Answer: A

NEW QUESTION # 357
Which of the following algorithms presents the most chaotic landscape on the loss surface?

A. MBGD
B. BGD
C. Stochastic gradient descent
D. MGD

Answer: C

Explanation:
Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.

NEW QUESTION # 358
......

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