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Huawei H13-336_V1.0 Exam Syllabus Topics:

Section	Weight	Objectives
AI Security and Governance	5%-10%	- Compliance and lifecycle governance 1. Compliance with AI industry regulations 2. Model audit and interpretability - Model security and privacy protection 1. Data desensitization and federated learning 2. Adversarial attack and defense
Model Deployment and Inference Optimization	15%-20%	- Inference engine and service deployment 1. Edge-cloud collaborative deployment 2. Batch processing and pipeline parallelism - Performance optimization and stability assurance 1. Inference latency and throughput tuning 2. Resource scheduling and fault tolerance
Data Engineering for AI	10%-15%	- Data collection, cleaning and annotation 1. Large-scale dataset construction 2. Data quality assessment and governance - Feature engineering and data management 1. Data version control and lifecycle management 2. Feature selection, transformation and encoding
AI Development Fundamentals and Large Model Technologies	15%-20%	- Large model pre-training and alignment technologies 1. Fine-tuning, RLHF, DPO and other alignment methods 2. Pre-training data construction and processing - Advanced machine learning and deep learning principles 1. Neural network optimization and regularization 2. Foundation model architectures (Transformer, MoE, etc.)
Intelligent Application Development	15%-20%	- RAG system design and implementation 1. Prompt engineering and generation control 2. Vector database selection and retrieval optimization - Agent and multi-modal application development 1. Multi-modal fusion and service integration 2. Tool calling and task planning
Huawei AI Platform and Model Development	25%-30%	- MindSpore framework and Ascend computing system 1. Distributed training strategies 2. Operator development and optimization - ModelArts full-lifecycle development 1. Model compression, quantization and pruning 2. Model training, evaluation and selection