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 04, 2026   Q&As: 0 Questions and Answers

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Huawei H13-336_V1.0 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: 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
          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: Intelligent Application Development15%-20%- Agent and multi-modal application development
                  • 1. Multi-modal fusion and service integration
                    • 2. Tool calling and task planning
                      - RAG system design and implementation
                      • 1. Prompt engineering and generation control
                        • 2. Vector database selection and retrieval optimization
                          Topic 4: 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 5: 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 compression, quantization and pruning
                                        • 2. Model training, evaluation and selection
                                          Topic 6: Data Engineering for AI10%-15%- Data collection, cleaning and annotation
                                          • 1. Data quality assessment and governance
                                            • 2. Large-scale dataset construction
                                              - Feature engineering and data management
                                              • 1. Feature selection, transformation and encoding
                                                • 2. Data version control and lifecycle management

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