Huawei H13-336_V1.0 : HCIE-AI Developer V1.0

  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Sep 14, 2026
  • Q & A: 0 Questions and Answers

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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
          Topic 2: 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. Edge-cloud collaborative deployment
                • 2. Batch processing and pipeline parallelism
                  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: 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. Data version control and lifecycle management
                                • 2. Feature selection, transformation and encoding
                                  Topic 5: AI Development Fundamentals and Large Model Technologies15%-20%- Large model pre-training and alignment technologies
                                  • 1. Pre-training data construction and processing
                                    • 2. Fine-tuning, RLHF, DPO and other alignment methods
                                      - Advanced machine learning and deep learning principles
                                      • 1. Foundation model architectures (Transformer, MoE, etc.)
                                        • 2. Neural network optimization and regularization
                                          Topic 6: AI Security and Governance5%-10%- Model security and privacy protection
                                          • 1. Data desensitization and federated learning
                                            • 2. Adversarial attack and defense
                                              - Compliance and lifecycle governance
                                              • 1. Model audit and interpretability
                                                • 2. Compliance with AI industry regulations

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