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Huawei H13-321_V2.0-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development strategy - Full-stack and all-scenario AI technology layout |
| Neural Network Basics | 4% | - Basic concepts of neural networks - Multilayer Perceptron (MLP) - Activation functions and regularization - Gradient descent and backpropagation |
| Natural Language Processing Lab Guide | 10% | - Text classification and NER implementation - Application integration and deployment - ModelArts NLP model training and tuning |
| Speech Processing Theory and Applications | 10% | - Automatic Speech Recognition (ASR) - Speech signal characteristics and processing - Text-to-Speech (TTS) technology - Acoustic and language modeling |
| Natural Language Processing Theory and Applications | 10% | - Word representation and embedding - BERT, GPT and pre-trained models - Text classification, NER, machine translation - RNN, LSTM, GRU, Transformer architecture |
| Speech Processing Lab Guide | 12% | - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service - ASR and TTS service development |
| Image Processing Theory and Applications | 26% | - Image processing fundamentals - OCR and visual application development - Convolutional Neural Networks (CNN) - Image classification, object detection, segmentation |
| Image Processing Lab Guide | 12% | - ModelArts-based image classification - Ascend-based deployment - Object detection and segmentation practice |
| Overview of ModelArts | 4% | - Data processing, training, deployment capabilities - Development environment and tool usage - ModelArts platform positioning and architecture |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. What are the algorithms for regular word segmentation? (Multiple choice)
A) Reverse Maximum Matching Method
B) Forward maximum matching
C) Polynomial Maximum Matching Method
D) Bidirectional Maximum Matching Method
2. The denoising effect of mean filtering is better than that of Gaussian filtering for the same size.
A) False
B) True
3. () cancels the two independent assumptions of HMM, treats label transfer and context input as global features, performs probability normalization globally, and solves the label bias and context feature missing problems of HMM.
4. The larger the template size, the better the denoising effect of the mean filter. When performing denoising, a large-sized template should be selected for mean filtering.
A) False
B) True
5. Which of the following can be done using Tensorboard? (Multiple choice)
A) Visualizing high-dimensional distributions
B) Automatic parameter tuning (what the algorithm needs to do)
C) Visualize the changes in loss and accuracy during deep learning training
D) Data Annotation (Tensorboard is not an annotation tool)
Solutions:
| Question # 1 Answer: A,B,D | Question # 2 Answer: A | Question # 3 Answer: Only visible for members | Question # 4 Answer: A | Question # 5 Answer: A,C |
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