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

H13-336_V1.0 real exams

Exam Code: H13-336_V1.0

Exam Name: HCIE-AI Developer V1.0

Updated: Aug 18, 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. Model audit and interpretability
    • 2. Compliance with AI industry regulations
      - Model security and privacy protection
      • 1. Data desensitization and federated learning
        • 2. Adversarial attack and defense
          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
                  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
                          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
                                  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
                                          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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