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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Speech Processing Lab Guide | 12% | - Speech application development on ModelArts |
| Topic 2: Overview of Huawei AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | |
| Topic 3: Image Processing Lab Guide | 12% | - ModelArts-based image development |
| Topic 4: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - NLP core concepts - Text processing and representation - Common NLP tasks and applications |
| Topic 5: Natural Language Processing Lab Guide | 10% | - NLP project practice on ModelArts |
| Topic 6: Theoretical Knowledge and Applications of Speech Processing | 10% | - Speech recognition and synthesis - Speech processing basics - Acoustic and language models |
| Topic 7: Theoretical Knowledge and Applications of Image Processing | 26% | - Computer vision overview - Image preprocessing and feature extraction - Convolutional neural networks - Image recognition, detection and segmentation |
| Topic 8: Neural Network Basics | 4% | - Network training and optimization - Deep learning fundamentals - Artificial neural network principles |
| Topic 9: Overview of ModelArts | 4% | - Development and deployment workflow - ModelArts platform architecture |
| Topic 10: ModelArts Platform Development Experiment | 10% |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. ModelArts is a one-stop AI development platform built around the AI development process. Users can customize and develop AI models based on ModelArts . ModelArts ' capabilities cover all aspects of AI development.
A) True
B) False
2. Which of the following options belongs to the preparation task before the third step of training the neural network (calculating the loss value between the predicted value and the target value through the loss function)? (Multiple choice)
A) Define the network structure and initialize network parameters
B) Model converges and training ends
C) Input data features and get predicted values through forward propagation
D) Update parameters by minimizing the loss value through gradient descent and back propagation algorithm
3. Which of the following options is not a method in the development of natural language processing?
A) Recursion-based methods
B) Statistical-based methods
C) Rule-based approach
D) Deep Learning-Based Methods
4. Which of the following belongs to the optimizer in gradient descent? (Multiple choice)
A) Adagrad (Adaptive Gradient Descent Optimizer)
B) momentum
C) Adam (improved version of the optimizer)
D) Adaboost (Ensemble learning idea)
5. Which of the following belongs to convolutional neural network? (Multiple choice)
A) VGG16
B) LSTM
C) RNN
D) Resnet
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,C | Question # 3 Answer: A | Question # 4 Answer: A,B,C | Question # 5 Answer: A,D |
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