Huawei H13-336_V1.0 Q&A - in .pdf

  • H13-336_V1.0 pdf
  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Aug 01, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
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
          AI Security and Governance5%-10%- Model security and privacy protection
          • 1. Adversarial attack and defense
            • 2. Data desensitization and federated learning
              - Compliance and lifecycle governance
              • 1. Model audit and interpretability
                • 2. Compliance with AI industry regulations
                  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
                          Huawei AI Platform and Model Development25%-30%- MindSpore framework and Ascend computing system
                          • 1. Distributed training strategies
                            • 2. Operator development and optimization
                              - ModelArts full-lifecycle development
                              • 1. Model training, evaluation and selection
                                • 2. Model compression, quantization and pruning
                                  Model Deployment and Inference Optimization15%-20%- Performance optimization and stability assurance
                                  • 1. Resource scheduling and fault tolerance
                                    • 2. Inference latency and throughput tuning
                                      - Inference engine and service deployment
                                      • 1. Batch processing and pipeline parallelism
                                        • 2. Edge-cloud collaborative deployment
                                          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
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