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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Text processing and representation - NLP core concepts - Common NLP tasks and applications |
| Topic 2: ModelArts Platform Development Experiment | 10% | |
| Topic 3: Image Processing Lab Guide | 12% | - ModelArts-based image development |
| Topic 4: Overview of ModelArts | 4% | - ModelArts platform architecture - Development and deployment workflow |
| Topic 5: Speech Processing Lab Guide | 12% | - Speech application development on ModelArts |
| Topic 6: Theoretical Knowledge and Applications of Image Processing | 26% | - Image preprocessing and feature extraction - Computer vision overview - Image recognition, detection and segmentation - Convolutional neural networks |
| Topic 7: Natural Language Processing Lab Guide | 10% | - NLP project practice on ModelArts |
| Topic 8: Theoretical Knowledge and Applications of Speech Processing | 10% | - Acoustic and language models - Speech recognition and synthesis - Speech processing basics |
| Topic 9: Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | |
| Topic 10: Neural Network Basics | 4% | - Deep learning fundamentals - Network training and optimization - Artificial neural network principles |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
What is the purpose of using gradient descent?
- A. Optimization parameters
- B. Finding label values
- C. Prediction results (forward calculation in neural network)
- D. Changing the model structure
Probability refers to the authenticity of a set of parameters given a result.
- A. False
- B. True
Convolutional layers are indispensable in the hierarchical architecture of convolutional neural networks.
- A. False
- B. True
Which of the following options is not an integrated learning algorithm in machine learning?
- A. Marking
- B. Stacking
- C. Bagging
- D. Boosting
What is the main difference between supervised and unsupervised algorithms?
- A. Test set
- B. Tags
- C. Data volume
- D. Validation Set
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