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Huawei H13-311_V3.5 (HCIA-AI V3.5) certification is designed for IT professionals who want to advance their careers in the AI field. HCIA-AI V3.5 certification can help individuals differentiate themselves in the job market and increase their earning potential. It is also a valuable credential for organizations that want to demonstrate their expertise in AI technologies and solutions.

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Huawei H13-311_V3.5 (HCIA-AI V3.5) Certification Exam is a comprehensive exam that covers a wide range of topics in the field of AI. H13-311_V3.5 exam is divided into different sections, each of which covers a specific aspect of AI, including data analysis, machine learning, natural language processing, and more. To pass the exam, you need to have a deep understanding of these topics and be able to apply your knowledge to real-world scenarios.

Huawei HCIA-AI V3.5 Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following are callback options provided by MindSpore?

A. SummaryCollector
B. TrainStep
C. ModelCheckpoint
D. LossMonitor

Answer: A,C,D

Explanation:
MindSpore provides several callback functions that can be used to monitor, modify, or control the behavior of the training process. These include:
SummaryCollector: Collects summaries such as loss and accuracy for visualization and monitoring.
ModelCheckpoint: Saves model parameters during or after training.
LossMonitor: Monitors the loss values during training and can stop training if certain conditions are met.
TrainStep is not a callback but rather a fundamental step in training.

NEW QUESTION # 92
Python regular expressions are a special sequence of characters that makes it easy to check if a string matches a pattern.

A. True
B. False

Answer: A

NEW QUESTION # 93
Which method is not supported in TensorFlow to define variables?

A. Calculated from the initial values of other variables
B. Constant
C. Random number
D. null

Answer: D

NEW QUESTION # 94
Which of the following descriptions of the depth feedforward network is correct?

A. Deep feedforward network 1s used to deal with linear problems.
B. Deep feedforward network 1s a kind of neural network.
C. The unit on the hidden layer of the deep feedforward network will have countless.
D. The deep feedforward network has only one hidden layer.

Answer: B

NEW QUESTION # 95
Which of the following are common gradient descent methods?

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

Answer: A,B,C

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 # 96
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