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Huawei H13-321_V2.0 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Speech Processing Theory and Applications	10%	- Text-to-Speech (TTS) technology - Automatic Speech Recognition (ASR) - Speech signal characteristics and processing - Acoustic and language modeling
Topic 2: Overview of ModelArts	4%	- ModelArts platform positioning and architecture - Development environment and tool usage - Data processing, training, deployment capabilities
Topic 3: Natural Language Processing Theory and Applications	10%	- BERT, GPT and pre-trained models - Text classification, NER, machine translation - Word representation and embedding - RNN, LSTM, GRU, Transformer architecture
Topic 4: Natural Language Processing Lab Guide	10%	- Application integration and deployment - ModelArts NLP model training and tuning - Text classification and NER implementation
Topic 5: Image Processing Lab Guide	12%	- ModelArts-based image classification - Ascend-based deployment - Object detection and segmentation practice
Topic 6: Neural Network Basics	4%	- Gradient descent and backpropagation - Multilayer Perceptron (MLP) - Activation functions and regularization - Basic concepts of neural networks
Topic 7: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio	2%	- Huawei AI development strategy - Full-stack and all-scenario AI technology layout
Topic 8: Speech Processing Lab Guide	12%	- Huawei Cloud Speech Interaction Service - ASR and TTS service development - ModelArts speech application deployment
Topic 9: Image Processing Theory and Applications	26%	- Image classification, object detection, segmentation - Image processing fundamentals - OCR and visual application development - Convolutional Neural Networks (CNN)

Huawei HCIP-AI-EI Developer V2.0 Sample Questions:

Question #1

Deep learning model training requires a large amount of data. In actual operation, data enhancement technology can be used to randomly add normal samples to the training image data set to expand the sample size of the data set.