Huawei HCIP-AI-EI Developer V2.0 : H13-321_V2.0

  • Exam Code: H13-321_V2.0
  • Exam Name: HCIP-AI-EI Developer V2.0
  • Q & A: 574 Questions and Answers

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Huawei H13-321_V2.0 Exam Syllabus Topics:

SectionObjectives
ModelArts Platform Overview- AI Development Platform
  • 1. Model Training
  • 2. Model Deployment
  • 3. AI Workflow Management
Huawei AI Development Strategy and AI Portfolio- Huawei AI Ecosystem
  • 1. All-Scenario AI Architecture
  • 2. Full-Stack AI Strategy
  • 3. Huawei Cloud EI Services
Natural Language Processing Theory and Applications- NLP Fundamentals
  • 1. Language Understanding
  • 2. Text Processing
  • 3. NLP Application Development
Speech Processing Theory and Applications- Speech AI Technologies
  • 1. Speech Recognition
  • 2. Speech Synthesis
  • 3. Speech Processing Scenarios
Image Processing Theory and Applications- Computer Vision Fundamentals
  • 1. Image Classification
  • 2. Object Detection
  • 3. Image Recognition Applications
Speech Processing Labs- Practical Development
  • 1. Speech Recognition Experiments
  • 2. Speech AI Applications
Natural Language Processing Labs- Practical Development
  • 1. NLP Model Applications
  • 2. Text Analysis Experiments
Image Processing Labs- Practical Development
  • 1. Image Processing Experiments
  • 2. Computer Vision Implementation
Neural Network Basics- Neural Network Fundamentals
  • 1. Artificial Neural Networks
  • 2. Common Neural Network Models
  • 3. Deep Learning Basics

Huawei HCIP-AI-EI Developer V2.0 Sample Questions:

Question #1

Which of the following options are Huawei Ascend AI chips? (Multiple choice)

  • A. Shengteng 510
  • B. Shengteng 310
  • C. Shengteng 710
  • D. Shengteng 910
Answer: B,D
Question #2

What does data preprocessing in speech tasks include? (Multiple choice)

  • A. Normalization
  • B. Framing
  • C. Endpoint Detection
  • D. Standardization
Answer: A,B,C,D
Question #3

Which modules does ModelArts cover? (Multiple choice)

  • A. Development Environment
  • B. AI Market
  • C. deployment and launch
  • D. Data Management
  • E. Model Management
Answer: A,B,C,D,E
Question #4

Compared with the batch gradient descent method, which of the following options are the advantages of the mini-batch gradient descent method? (Multiple choice)

  • A. Short time consumption
  • B. Fast calculation speed in one parameter update
  • C. Stability
  • D. No gradient disappearance
Answer: A,B
Question #5

What are the classifications of machine learning methods? (Multiple choice)

  • A. Supervised Learning
  • B. Unsupervised Learning
  • C. Self-supervised learning
  • D. Reinforcement Learning
  • E. Semi-supervised learning
Answer: A,B,C,D,E

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