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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Speech signal processing foundation - Speech feature extraction - Application cases |
| Image Processing Lab Guide | 12% | - Image processing model development and deployment - Development environment setup - Performance optimization and testing |
| Natural Language Processing Lab Guide | 10% | - NLP model training and evaluation - End-to-end application development - Text preprocessing and feature engineering |
| Theoretical Knowledge and Applications of Image Processing | 26% | - Image classification, detection and segmentation - Typical application scenarios - Feature extraction and representation - Image preprocessing technology |
| Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Text processing and representation - Language model and semantic understanding - Practical application - Machine translation, text generation and other technologies |
| Overview of ModelArts | 4% | - Core functions and service modules - Basic operation process - ModelArts positioning and architecture |
| Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - All-scenario AI solutions - Huawei AI development layout - Full-stack AI technology system |
| Neural Network Basics | 4% | - Basic concepts of neural networks - Common neural network structures - Training and optimization methods |
| Speech Processing Lab Guide | 12% | - Speech data processing practice - Application deployment and verification - Speech model building and tuning |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
A) FALSE
B) TRUE
2. Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
A) It can process high-resolution images to enhance classification accuracy.
B) The self-attention mechanism is used to capture global features of images, improving classification accuracy.
C) It can handle small datasets with minimal labeling required.
D) It achieves fast convergence without using pre-trained models.
3. In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
A) FALSE
B) TRUE
4. The natural language processing field usually uses distributed semantic representation to represent words.
Each word is no longer a completely orthogonal 0-1 vector, but a point in a multi-dimensional real number space, which is specifically represented as a real number vector.
A) FALSE
B) TRUE
5. In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?
A) Grayscale compression
B) Gamma correction
C) Inversion
D) Grayscale stretching
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: B |
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