Exam Code: H13-336_V1.0
Exam Name: HCIE-AI Developer V1.0
Certification Provider: Huawei
Corresponding Certification: Huawei-certification
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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Engineering for AI10%-15%- Data collection, cleaning and annotation
  • 1. Large-scale dataset construction
    • 2. Data quality assessment and governance
      - Feature engineering and data management
      • 1. Feature selection, transformation and encoding
        • 2. Data version control and lifecycle management
          Topic 2: 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
                  Topic 3: Model Deployment and Inference Optimization15%-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
                          Topic 4: 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. Compliance with AI industry regulations
                                • 2. Model audit and interpretability
                                  Topic 5: Huawei AI Platform and Model Development25%-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
                                          Topic 6: AI Development Fundamentals and Large Model Technologies15%-20%- Advanced machine learning and deep learning principles
                                          • 1. Foundation model architectures (Transformer, MoE, etc.)
                                            • 2. Neural network optimization and regularization
                                              - Large model pre-training and alignment technologies
                                              • 1. Fine-tuning, RLHF, DPO and other alignment methods
                                                • 2. Pre-training data construction and processing

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