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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
AI Development Fundamentals and Large Model Technologies15%-20%- Advanced machine learning and deep learning principles
  • 1. Neural network optimization and regularization
    • 2. Foundation model architectures (Transformer, MoE, etc.)
      - Large model pre-training and alignment technologies
      • 1. Pre-training data construction and processing
        • 2. Fine-tuning, RLHF, DPO and other alignment methods
          AI Security and Governance5%-10%- Compliance and lifecycle governance
          • 1. Model audit and interpretability
            • 2. Compliance with AI industry regulations
              - Model security and privacy protection
              • 1. Adversarial attack and defense
                • 2. Data desensitization and federated learning
                  Intelligent Application Development15%-20%- Agent and multi-modal application development
                  • 1. Tool calling and task planning
                    • 2. Multi-modal fusion and service integration
                      - RAG system design and implementation
                      • 1. Prompt engineering and generation control
                        • 2. Vector database selection and retrieval optimization
                          Huawei AI Platform and Model Development25%-30%- ModelArts full-lifecycle development
                          • 1. Model training, evaluation and selection
                            • 2. Model compression, quantization and pruning
                              - MindSpore framework and Ascend computing system
                              • 1. Operator development and optimization
                                • 2. Distributed training strategies
                                  Data Engineering for AI10%-15%- Feature engineering and data management
                                  • 1. Feature selection, transformation and encoding
                                    • 2. Data version control and lifecycle management
                                      - Data collection, cleaning and annotation
                                      • 1. Data quality assessment and governance
                                        • 2. Large-scale dataset construction
                                          Model Deployment and Inference Optimization15%-20%- Performance optimization and stability assurance
                                          • 1. Inference latency and throughput tuning
                                            • 2. Resource scheduling and fault tolerance
                                              - Inference engine and service deployment
                                              • 1. Batch processing and pipeline parallelism
                                                • 2. Edge-cloud collaborative deployment

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