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Huawei H13-336_V1.0 Braindumps - in .pdf Free Demo

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

SectionWeightObjectives
Topic 1: 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. Operator development and optimization
        • 2. Distributed training strategies
          Topic 2: 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. Data desensitization and federated learning
                • 2. Adversarial attack and defense
                  Topic 3: Intelligent Application Development15%-20%- RAG system design and implementation
                  • 1. Prompt engineering and generation control
                    • 2. Vector database selection and retrieval optimization
                      - Agent and multi-modal application development
                      • 1. Multi-modal fusion and service integration
                        • 2. Tool calling and task planning
                          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. Resource scheduling and fault tolerance
                                • 2. Inference latency and throughput tuning
                                  Topic 5: 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 6: Data Engineering for AI10%-15%- Feature engineering and data management
                                          • 1. Data version control and lifecycle management
                                            • 2. Feature selection, transformation and encoding
                                              - Data collection, cleaning and annotation
                                              • 1. Data quality assessment and governance
                                                • 2. Large-scale dataset construction

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