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Huawei H13-311_V3.5 (HCIA-AI V3.5) Certification Exam is an industry-recognized certification offered by Huawei, a leading global provider of information and communications technology (ICT) infrastructure and smart devices. HCIA-AI V3.5 certification exam is designed to validate the skills and knowledge of individuals in the field of artificial intelligence (AI). It is an entry-level certification that provides a strong foundation for individuals who want to pursue a career in AI.

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Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is a professional certification that measures the ability of an individual to install, configure, and manage Huawei AI products and technologies. It is designed to validate the skills and knowledge required to work with AI technologies, including machine learning, deep learning, and natural language processing. H13-311_V3.5 exam is intended for IT professionals, network engineers, and developers who want to specialize in AI and gain recognition for their expertise.

Huawei H13-311_V3.5 exam is a comprehensive assessment of an individual's knowledge and skills in AI and ML. H13-311_V3.5 exam consists of multiple-choice and scenario-based questions that test the candidate's ability to apply their knowledge and skills to solve real-world problems. H13-311_V3.5 exam is conducted in a proctored environment to ensure the integrity and authenticity of the certification.

Reference: https://edu.huaweicloud.com/intl/en-us/certificationindex/career/aie.html

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Huawei H13-311_V3.5 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Industry Applications10%- Computer Vision
  • 1. Image Segmentation
  • 2. Object Detection
  • 3. Image Classification
- Natural Language Processing
  • 1. Named Entity Recognition
  • 2. Text Classification
  • 3. Machine Translation
- Speech Recognition
  • 1. Speaker Recognition
  • 2. Speech to Text
Topic 2: Deep Learning Fundamentals25%- Neural Network Basics
  • 1. Perceptron
  • 2. Backpropagation
  • 3. Activation Functions
  • 4. Forward Propagation
- Deep Neural Network
  • 1. Regularization Techniques
  • 2. Recurrent Neural Networks (RNN)
  • 3. Convolutional Neural Networks (CNN)
  • 4. Optimizer Algorithms
Topic 3: Huawei AI Ecosystem30%- HiLens Platform
  • 1. HiLens Framework Architecture
  • 2. HiLens Skill Development
  • 3. HiLens Application Scenarios
- ModelArts Platform
  • 1. Model Training
  • 2. Model Deployment
  • 3. Data Preparation
  • 4. ModelArts Overview
  • 5. Automated Machine Learning (AutoML)
- Atlas Computing Platform
  • 1. MindSpore Framework
  • 2. CANN Architecture
  • 3. Huawei Ascend AI Processor
Topic 4: AI Development Basics20%- AI Project Workflow
  • 1. Model Selection and Training
  • 2. Data Collection and Preprocessing
  • 3. Model Evaluation and Optimization
  • 4. Problem Definition
- Python Programming for AI
  • 1. Pandas Basics
  • 2. Data Visualization
  • 3. NumPy Basics
Topic 5: AI Fundamentals15%- AI Overview
  • 1. AI Application Scenarios
  • 2. AI Three Elements (Data, Algorithm, Computing Power)
  • 3. Definition and History of AI
- Machine Learning Basics
  • 1. Common Machine Learning Algorithms
  • 2. Unsupervised Learning
  • 3. Reinforcement Learning
  • 4. Supervised Learning
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