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Huawei H13-321_V2.0 Exam Syllabus Topics:

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

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

What is the main basis for image classification?

  • A. Image size
  • B. Shape
  • C. Color
  • D. Features
Answer: D

In the Hidden Markov Model, which of the following options can be calculated using the Gaussian Mixture Model (GMM)?

  • A. Initial Probability
  • B. Observation Probability
  • C. Transition Probability
  • D. Language Probability
Answer: B

A photo has many small noise points during storage. The best denoising effect is to perform () operation on the scanned copy.

  • A. Laplace filtering
  • B. Mean Filter
  • C. Gaussian filtering
  • D. Median Filter
Answer: D

The v component of the HSV color space and the Y component of the YUV color space both represent the brightness of the image and can be separated as the result of grayscale conversion of the color image.

  • A. False
  • B. True
Answer: B

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

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

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