The H13-311_V3.5 exam is the latest and most updated version for the HCIA-AI V3.5 Certification. This certification is essential for those looking to advance their career in the field of artificial intelligence. Passcert has newly released comprehensive HCIA-AI V3.5 Certification H13-311_V3.5 Dumps that will allow you to prepare for the exam in a more thorough and effective way. These HCIA-AI V3.5 Certification H13-311_V3.5 Dumps include detailed questions and answers, practice tests, and study guides that cover all the key topics and concepts. If you are diligently going through all of our HCIA-AI V3.5 Certification H13-311_V3.5 Dumps, then you will be well-equipped and confident to clear the H13-311_V3.5 exam on the first attempt, ensuring your success and paving the way for future opportunities in the AI field.

HCIA-AI V3.5 Certification

Passing the HCIA-AI V3.5 certification will indicate that you: (1) Understand the AI development history, Huawei Ascend AI system, Huawei full-stack all-scenario AI strategy, cutting-edge AI applications, and algorithms related to traditional machine learning and deep learning (2) Be able to build, train, and deploy neural networks by using the MindSpore development framework (3) Be ready to take on AI positions in sales, marketing, product management, project management, technical support, and more

Target Audience

Personnel who hope to become AI engineers

Personnel who hope to obtain an HCIA-AI certificate

Personnel who hope to know how to use, manage, and maintain Huawei AI products and AI services

Prerequisites

Possess the basic knowledge of advanced mathematics, and have studied the pre-course of "Math Basics".

Familiar with Python language, and have studied the pre-course of "Python Basics".

Huawei HCIA-AI V3.5 Certification Exam Overview

Exam Code	H13-311
Exam Name	HCIA-AI
Exam Language	ENU/CHS
Question Type	Single Answer, Multiple Answer, True-false Question, Fill in the blank answers, Drag and drop item
Exam Fees	200 USD
Exam Duration	90 min
Passing Score	600 / 1000

Key Points Percentage

Key Points	Percentage
AI Overview	15%
Machine Learning Overview	20%
Deep Learning Overview	25%
AI Development Framework	20%
Introduction to Huawei AI Platforms	14%
Cutting-edge AI applications	6%

AI Overview

AI Overview

Application Fields of AI

Huawei's AI Development Strategy

Controversies Over AI and Its Future

Machine Learning Overview

Machine Learning Algorithms

Types of Machine Learning

Machine Learning Process

Important Machine Learning Concepts

Common Machine Learning Algorithms

Deep Learning Overview

Deep Learning

Training Rules

Activation Functions

Normalization

Optimizers

Neural Network Types

AI Development Framework

AI Framework Development

MindSpore

MindSpore Features

MindSpore Development Components

AI Application Development Process

Introduction to Huawei AI Platforms

Huawei Ascend Computing Platform

Huawei Cloud EI Platform

Huawei Device AI Platforms

Cutting-edge AI applications

Reinforcement Learning

GAN

Knowledge Graph

Intelligent Driving

Quantum Computing and Machine Learning

Share HCIA-AI V3.5 Certification H13-311_V3.5 Free Dumps

1. Which of the following technologies is commonly used for image feature extraction and related research?

A. Convolutional neural network

B. Naive Bayes classification algorithm

C. Long short-term memory (LSTM) network

D. Word2Vec

Answer: A

2. "Batch inference is a batch job that performs inference on batch data. There is no need for model training before using batch inference." Which of the following is true about this statement?

A. This statement is correct. With batch inference, training is no longer required.

B. This statement is correct. Inference means the end of training.

C. This statement is incorrect. Model training is required before inference is performed.

D. This statement is incorrect. No training is required before batch inference.

Answer: C

3. Consider a scenario where a machine learning algorithm is used to filter spam. According to the definition of machine learning, which of the following describes the experience E?

A. Spam filtering

B. Accuracy of spam filtering

C. All tagged spam and genuine emails in the past three years

D. Email addresses

Answer: C

4. A computer uses labeled images to learn and determine which images contain apples and which contain pears. Which of the following types of machine learning is most applicable to this scenario?

A. Supervised learning

B. Unsupervised learning

C. Semi-supervised learning

D. Reinforcement learning

Answer: A

5. Which of the following statements is true about classification models and regression models in machine learning?

A. For regression problems, the output variables are discrete values. For classification problems, the output variables are continuous values.

B. The most commonly used indicators for evaluating regression and classification problems are accuracy and recall rate.

C. There may be overfitting in both regression and classification problems.

D. Logistic regression is a typical regression model.

Answer: C

6. During neural network training, which of the following values is continuously updated by using the gradient descent method to minimize the loss function?

A. Hyperparameter

B. Feature

C. Number of samples

D. Parameter

Answer: D

7. Which of the following points constitute a support vector of the SVM algorithm without considering regularization terms?

A. Points on the separating hyperplane

B. Farthest points from the separating hyperplane

C. Points closest to the separating hyperplane

D. Points of a certain type

Answer: C

8. Which of the following are false about convolutional neural networks?

A. A convolutional neural network may contain convolutional, pooling, and fully connected layers.

B. Convolution kernels cannot extract global features of images.

C. Common pooling includes max pooling and average pooling.

D. When an image is processed, convolution is implemented by using a scanning window.

Answer: B

9. Overfitting problems can be avoided through dataset expansion. Which of the following statements is true about dataset expansion?

A. The larger the dataset, the lower the probability of overfitting.

B. The larger the dataset, the higher the probability of overfitting.

C. The smaller the dataset, the lower the probability of overfitting.

D. The probability of overfitting decreases when the dataset increases or decreases.

Answer: A

10. Which of the following is NOT a complexity feature of Al computing?

A. Mixed precision computing

B. Parallel data and computing

C. Parallel communication and computing

D. Parallel processing of structured and unstructured data

Answer: D

11. Which of the following statements about the running process of the MindArmour subsystem is false?

A. Configuration policies: Define test policies based on threat vectors and trustworthiness certification requirements and select appropriate test data generation methods.

B. Fuzzing execution: Generate trusted test data randomly based on the model coverage and configuration policies.

C. Evaluation report: Generate, an evaluation report based on built-in or user-defined trustworthiness metrics.

D. Trustworthiness enhancement: Use preset methods to enhance the trustworthiness of Al models.

Answer: B

12. Which of the following are topics of speech processing research?

A. Speech recognition

B. Voice processing

C. Speech wake-up

D. Voiceprint recognition

Answer: ABCD