Huawei HCIE-AI Developer V1.0 H13-336_V1.0 Certified Exam Dumps

H13-336_V1.0 Exam Dumps

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Certification Provider: Huawei
Exam Code / Number: H13-336_V1.0
Exam Name: HCIE-AI Developer V1.0
Exam Questions: 0
Corresponding Certification: Huawei-certification

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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
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. Tool calling and task planning
        • 2. Multi-modal fusion and service integration
          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
                  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. Compliance with AI industry regulations
                        • 2. Model audit and interpretability
                          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
                                  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
                                          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. Neural network optimization and regularization
                                                • 2. Foundation model architectures (Transformer, MoE, etc.)


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