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HCIA-AI V3.0

Last Update 2 hours ago Total Questions : 369

The HCIA-AI V3.0 content is now fully updated, with all current exam questions added 2 hours ago. Deciding to include H13-311_V3.0 practice exam questions in your study plan goes far beyond basic test preparation.

You'll find that our H13-311_V3.0 exam questions frequently feature detailed scenarios and practical problem-solving exercises that directly mirror industry challenges. Engaging with these H13-311_V3.0 sample sets allows you to effectively manage your time and pace yourself, giving you the ability to finish any HCIA-AI V3.0 practice test comfortably within the allotted time.

Question # 21

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 linear functions

B.

Activation functions are non-linear functions

C.

The activation function is partly a nonlinear function, partly a linear function

D.

Most of the activation functions are nonlinear functions, and a few are linear functions

Question # 22

Which of the following does not belong to automatic hyperparameter optimization algorithm?

A.

Grid search

B.

Random gradient descent

C.

Random search

D.

Model-based hyper parameter optimization

Question # 23

What are the algorithms supported by Tensorflow? (Multiple Choice}

A.

GNN

B.

ZNN

C.

RNN (Rig ht Answers)

D.

HUT

Question # 24

Where should the labeled data be placed in the confrontation generation network?

A.

As the output value of the generated model

B.

As the input value of the discriminant model

C.

As the output value of the discriminant model

D.

As input value for generative model

Question # 25

On-Device Execution, that is, the entire image is offloaded and executed, and the computing power of the Yiteng chip can be fully utilized, which can greatly reduce the interaction overhead, thereby increasing the

accelerator occupancy rate. On-Device The following description is wrong?

A.

MindSpore Realize decentralized autonomy through adaptive graph optimization driven by gradient data A11 Reduce, Gradient aggregation is in step, and calculation and communication are fully streamlined

B.

Challenges of model execution under super chip computing power: Memory wall problems, high interaction overhead, and difficulty in data supply. Partly in Host Executed, partly in Device Execution, interaction

overhead is even much greater than execution overhead, resulting in low accelerator occupancy

C.

MindSpore Through the chip-oriented depth map optimization technology, the synchronization wait is less, and the " data computing communication " is maximized. The parallelism of “trust”, compared with training

performance Host Side view scheduling method is flat

D.

The challenge of distributed gradient aggregation under super chip computing power:ReslNet50 Single iteration 20ms Time will be generated The synchronization overhead of heart control and the communication

overhead of frequent synchronization. Traditional methods require 3 Synchronization completed A11 Reduce, Data-driven method autonomy A11 Reduce, No control overhead

Question # 26

The following about the standard RNN Model, the correct statement is?

A.

There is no one-to-one model structure

B.

Do not consider the time direction when backpropagating

C.

There is no many-to-many model structure

D.

There will be a problem of attenuation of long-term transmission and memory information

Question # 27

TensorFlow It is an end-to-end open source platform for machine learning and deep learning.

A.

TRUE

B.

FALSE

Question # 28

What of the following does belong to convolutional neural network (CNN)? (Multiple Choice)

A.

VGGNet

B.

ResNet

C.

AlexNet

D.

GoogleNet

Question # 29

The for loop statement in the Python language can iterate through the items in any sequence.

A.

True

B.

False

Question # 30

In deep learning tasks, when encountering data imbalance problems, which of the following methods can we use to solve the problem?

A.

batch deletion

B.

Random oversampling

C.

Synthetic sampling

D.

Random undersampling

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