HCIA-AI V3.0 H13-311_v3.0 Questions and Answers

 Looking for best preparation for HCIA-AI V3.0 certification exam? The latest HCIA-AI V3.0 H13-311_v3.0 Study Material is new released by PassQuestion team which contains real H13-311_v3.0 questions and answers for you to practice, then you will be more confident to prepare for your HCIA-AI certification H13-311_v3.0 exam. PassQuestion can promise that you can 100% pass your HCIA-AI V3.0 H13-311_v3.0 exam in the first time.

HCIA-AI V3.0 Certification Exam Overview

Passing the HCIA-AI V3.0 certification will indicate that you: 1)have mastered the AI development history, the Huawei Ascend AI system, the full-stack all-scenario AI strategy，and the algorithms related to traditional machine learning and deep learning. 2) are able to build, train, and deploy neural networks by using development frameworks TensorFlow and MindSpore. 3) competent for sales, marketing, product manager, project management, technical support, and other AI positions.

Exam Contents

The HCIA-AI V3.0 exam covers:
AI Overview
Machine Learning Overview
Deep Learning Overview
Mainstream Development Frameworks for AI
Huawei AI Development Framework MindSpore
Huawei AI Computing Platform Atlas
Huawei Open AI Platform for Smart Devices
HUAWEI CLOUD Enterprise Intelligence Application Platform
Comprehensive AI Experiment

HCIA-AI V3.0 Exam Key Points

1 AI Overview

1.1 AI Overview
1.2 Technical Fields and Application Fields of AI
1.3 Huawei's AI Development Strategy
1.4 AI Disputes
1.5 Future Prospects of AI

2 Machine Learning Overview

2.1 Machine Learning Definition
2.2 Machine Learning Types
2.3 Machine Learning Process
2.4 Other Key Machine Learning Methods
2.5 Common Machine Learning Algorithms
2.6 Case Study

3 Deep Learning Overview

3.1 Deep Learning Summary
3.2 Training Rules
3.3 Activation Function
3.4 Regularization
3.5 Optimizer
3.6 Types of Neural Network
3.7 Common Problems

4 Mainstream Development Frameworks for AI

4.1 Mainstream Development Frameworks
4.2 TensorFlow 2.x Basics
4.3 Common Modules of TensorFlow 2.x
4.4 Basic Steps of Deep Learning Development

5 Huawei AI Development Framework MindSpore

5.1 MindSpore Development Framework
5.2 MindSpore Development and Application

6 Huawei AI Computing Platform Atlas

6.1 Overview of AI Chips
6.2 Hardware Architecture of Ascend Chips
6.3 Software Architecture of Ascend Chips
6.4 Huawei Atlas AI Computing Platform
6.5 Industry Applications of Atlas

7 Huawei Open AI Platform for Smart Devices

7.1 AI Industry Ecosystem
7.2 Huawei HiAI Platform
7.3 Developing Apps Based on Huawei HiAI Platform

8 HUAWEI CLOUD Enterprise Intelligence Application Platform

8.1 Overview of HUAWEI CLOUD EI
8.2 ModelArts
8.3 HUAWEI CLOUD EI Solutions

9 Comprehensive AI Experiment

9.1 Machine Learning Experiment
9.2 Mainstream Development Framework and Deep Learning Experiment
9.3 ModelArts Experiment Guide

View Online HCIA-AI V3.0 H13-311_v3.0 Free Questions

1.Faced with the challenge of achieving efficient distributed training for ultra-large-scale models, MindSpore is handled as?
A. Automatic parallel
B. Serial
C. Manual parallel
Answer: A

2.Which of the following is not the difference between Python 2 and Python 3?
A. print
B. Unicode
C. import
D. xrange
Answer: C

3.Which of the following options is not central to linear algebra?
A. Probability theory
B. Linear transformation
C. Matrix theory
D. Vector space
Answer: A

4.The Python dictionary is widely identified by "{}". and the internal data consists of the key and its corresponding value
A. True
B. False
Answer: A

5.In the process of training the neural network, we use the gradient descent method to continuously update which value, which makes the loss Function minimization?
A. Number of samples
B. Eigenvalues
C. Hyperparameter
D. parameter 
Answer: D

6.Huawei Machine learning Service MLS MLS is a one-stop platform that supports the entire process of data analysis.
Which of the following is not a feature of MLS?
A. A rich library of machine learning algorithms.
B. machine learning program is intuitive and easy to use.
C. Distributed and scalable big data computing engine.
D. Support for the R language but does not support the Python language
Answer: D