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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Inference latency and throughput tuning
        • 2. Resource scheduling and fault tolerance
          Topic 2: 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. Fine-tuning, RLHF, DPO and other alignment methods
                • 2. Pre-training data construction and processing
                  Topic 3: 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. Distributed training strategies
                        • 2. Operator development and optimization
                          Topic 4: 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. Vector database selection and retrieval optimization
                                • 2. Prompt engineering and generation control
                                  Topic 5: AI Security and Governance5%-10%- Compliance and lifecycle governance
                                  • 1. Compliance with AI industry regulations
                                    • 2. Model audit and interpretability
                                      - Model security and privacy protection
                                      • 1. Adversarial attack and defense
                                        • 2. Data desensitization and federated learning
                                          Topic 6: 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. Large-scale dataset construction
                                                • 2. Data quality assessment and governance

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