Huawei HCIE-AI Developer V1.0 - H13-336_V1.0

Huawei H13-336_V1.0 test insides dumps
  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Sep 12, 2026
  • Q & A: 0 Questions and Answers
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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
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
          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 compression, quantization and pruning
                • 2. Model training, evaluation and selection
                  AI Security and Governance5%-10%- Model security and privacy protection
                  • 1. Data desensitization and federated learning
                    • 2. Adversarial attack and defense
                      - Compliance and lifecycle governance
                      • 1. Model audit and interpretability
                        • 2. Compliance with AI industry regulations
                          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
                                  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. Large-scale dataset construction
                                        • 2. Data quality assessment and governance
                                          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

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