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

  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Jul 19, 2026     Q & A: 0 Questions and Answers

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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
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
          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
                  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
                          Huawei AI Platform and Model Development25%-30%- ModelArts full-lifecycle development
                          • 1. Model compression, quantization and pruning
                            • 2. Model training, evaluation and selection
                              - MindSpore framework and Ascend computing system
                              • 1. Distributed training strategies
                                • 2. Operator development and optimization
                                  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. Batch processing and pipeline parallelism
                                        • 2. Edge-cloud collaborative deployment
                                          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. Pre-training data construction and processing
                                                • 2. Fine-tuning, RLHF, DPO and other alignment methods

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