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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Speech Processing Lab Guide | 12% | - Huawei Cloud Speech Interaction Service - ModelArts speech application deployment - ASR and TTS service development |
| Natural Language Processing Theory and Applications | 10% | - BERT, GPT and pre-trained models - Word representation and embedding - RNN, LSTM, GRU, Transformer architecture - Text classification, NER, machine translation |
| Image Processing Lab Guide | 12% | - ModelArts-based image classification - Ascend-based deployment - Object detection and segmentation practice |
| Natural Language Processing Lab Guide | 10% | - ModelArts NLP model training and tuning - Text classification and NER implementation - Application integration and deployment |
| Overview of ModelArts | 4% | - ModelArts platform positioning and architecture - Development environment and tool usage - Data processing, training, deployment capabilities |
| Neural Network Basics | 4% | - Gradient descent and backpropagation - Activation functions and regularization - Basic concepts of neural networks - Multilayer Perceptron (MLP) |
| Speech Processing Theory and Applications | 10% | - Speech signal characteristics and processing - Text-to-Speech (TTS) technology - Automatic Speech Recognition (ASR) - Acoustic and language modeling |
| Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
| Image Processing Theory and Applications | 26% | - Convolutional Neural Networks (CNN) - OCR and visual application development - Image processing fundamentals - Image classification, object detection, segmentation |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. Automatic learning is a service provided by ModelArts. It can automatically design models, automatically adjust parameters, automatically train, automatically compress and deploy models based on labeled data. It does not require code writing and model development experience, and can build AI models from scratch.
A) True
B) False
2. ModelArts model training process
3. Which of the following options belong to the internal evaluation methods in word vector evaluation? (Multiple choice)
A) mAP
B) Analogy Task
C) Accuracy
D) Similarity Task
4. How difficult is speech recognition?
A) All of the above
B) Scenarios
C) Geographical factors
D) Personal characteristics
5. A function of tensorflow2.0 needs to display the results and information of intermediate training when outputting. Please write which parameter ________ in the function should be set to True.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: Only visible for members | Question # 3 Answer: B,D | Question # 4 Answer: A | Question # 5 Answer: Only visible for members |
