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

  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Jul 22, 2026
  • Q&As: 0 Questions and Answers

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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
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
          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
                  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
                          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
                                  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. Tool calling and task planning
                                        • 2. Multi-modal fusion and service integration
                                          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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