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H13-311_V3.0 HCIA-AI V3.0 is now Stable and With Pass Result | Test Your Knowledge for Free

H13-311_V3.0 Practice Questions

HCIA-AI V3.0

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Question # 1

In a neural network based on connectionism, each node can express a specific meaning.

Options:

A.  

TRUE

B.  

FALSE

Discussion 0
Question # 2

Which of the following is the computing model of TensorFlow?

Options:

A.  

Calculation chart

B.  

Tensor

C.  

Conversation

D.  

Variable

Discussion 0
Question # 3

Which of the following options is not a reason for traditional machine learning algorithms to promote the development of deep learning?

Options:

A.  

Dimensional disaster

B.  

local invariance and smooth regularization

C.  

Manifold learning

D.  

Feature Engineering

Discussion 0
Question # 4

Artificial intelligence is a new technical science that studies and develops theories, methods and application systems for simulating, extending and extending human intelligence. It is one of the

core research areas of machine learning.

Options:

A.  

True

B.  

False

Discussion 0
Question # 5

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

Options:

A.  

TRUE

B.  

FALSE

Discussion 0
Question # 6

What is in the category of artificial intelligence? (Multiple choice)

Options:

A.  

Action

B.  

Perception

C.  

Cognition

D.  

None of the above

Discussion 0
Question # 7

HiAI What are the advantages of mobile computing platforms?

Options:

A.  

Quick source code

B.  

Diverse tool chain

C.  

Complete documentation

D.  

plentiful API

Discussion 0
Question # 8

The naive Bayes algorithm does not require independent and identical distribution among sample features.

Options:

A.  

TRUE

B.  

FALSE

Discussion 0
Question # 9

Python tuples are identified by "()'' and internal elements are separated by ":".

Options:

A.  

True

B.  

False

Discussion 0
Question # 10

The following options belong to keras under estimator The encapsulation methods are?

Options:

A.  

Assessment

B.  

training

C.  

prediction

D.  

Output model

Discussion 0
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