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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Overview of ModelArts | 4% | - Development and deployment workflow - ModelArts platform architecture |
| ModelArts Platform Development Experiment | 10% | |
| Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | |
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Speech processing basics - Acoustic and language models |
| Neural Network Basics | 4% | - Deep learning fundamentals - Artificial neural network principles - Network training and optimization |
| Image Processing Lab Guide | 12% | - ModelArts-based image development |
| Speech Processing Lab Guide | 12% | - Speech application development on ModelArts |
| Theoretical Knowledge and Applications of Image Processing | 26% | - Image recognition, detection and segmentation - Convolutional neural networks - Computer vision overview - Image preprocessing and feature extraction |
| Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Common NLP tasks and applications - Text processing and representation - NLP core concepts |
| Natural Language Processing Lab Guide | 10% | - NLP project practice on ModelArts |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
The core problem of computer vision ()
Reveal Solution Discussion 0Correct Answer:
Image Classification
Which of the following is not a NLP language feature?
- A. Single word features
- B. Texture features
- C. Text Features
- D. Contextual word features
Correct Answer: B 🗳️
The back propagation algorithm is an algorithm used to calculate gradients in neural networks.
- A. False
- B. True
Correct Answer: B 🗳️
Keyword extraction algorithms are divided into supervised and unsupervised categories.
- A. False
- B. True
Correct Answer: B 🗳️
Which of the following problems cannot be solved by a single-layer perceptron?
- A. XOR problem
- B. Simple binary classification problem
- C. AND Problem
- D. OR Problem
Correct Answer: A 🗳️
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