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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Theoretical Knowledge and Applications of Image Processing | 26% | - Image preprocessing and feature extraction - Convolutional neural networks - Computer vision overview - Image recognition, detection and segmentation |
| Speech Processing Lab Guide | 12% | - Speech application development on ModelArts |
| Natural Language Processing Lab Guide | 10% | - NLP project practice on ModelArts |
| Neural Network Basics | 4% | - Deep learning fundamentals - Artificial neural network principles - Network training and optimization |
| Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | |
| Overview of ModelArts | 4% | - ModelArts platform architecture - Development and deployment workflow |
| Theoretical Knowledge and Applications of Speech Processing | 10% | - Acoustic and language models - Speech processing basics - Speech recognition and synthesis |
| ModelArts Platform Development Experiment | 10% | |
| Image Processing Lab Guide | 12% | - ModelArts-based image development |
| Theoretical Knowledge and Applications of Natural Language Processing | 10% | - NLP core concepts - Text processing and representation - Common NLP tasks and applications |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
Question 1
The output of most optical sensors is a continuous signal. The computer can only perform digital image processing after the continuous signal is converted into a discrete signal.
A. True
B. False
Question 2
When applying image processing technology, it is important to identify which task the problem belongs to: image classification, target detection, image segmentation, or target tracking. Algorithms cannot be mixed.
A. True
B. False
Question 3
Compared with the Sigmoid activation function, which of the following options are the advantages of ReLU? (Multiple choice)
A. Alleviating the vanishing gradient problem
B. Globally configurable
C. Alleviating the gradient explosion problem
D. Save computational effort
Question 4
Cifar10 is a built-in dataset of keras.
A. True
B. False
Question 5
Word vector is also called ()
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: A,D | Question 4 Answer: A | Question 5 Answer: Only visible for members |
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