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Huawei H13-336_V1.0 Exam Syllabus Topics:

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

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