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Huawei H13-336_V1.0 Practice Q&A's

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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
Intelligent Application Development15%-20%- Agent and multi-modal application development
  • 1. Tool calling and task planning
    • 2. Multi-modal fusion and service integration
      - RAG system design and implementation
      • 1. Vector database selection and retrieval optimization
        • 2. Prompt engineering and generation control
          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
                  AI Development Fundamentals and Large Model Technologies15%-20%- Large model pre-training and alignment technologies
                  • 1. Fine-tuning, RLHF, DPO and other alignment methods
                    • 2. Pre-training data construction and processing
                      - Advanced machine learning and deep learning principles
                      • 1. Foundation model architectures (Transformer, MoE, etc.)
                        • 2. Neural network optimization and regularization
                          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%- Compliance and lifecycle governance
                                  • 1. Model audit and interpretability
                                    • 2. Compliance with AI industry regulations
                                      - Model security and privacy protection
                                      • 1. Adversarial attack and defense
                                        • 2. Data desensitization and federated learning
                                          Model Deployment and Inference Optimization15%-20%- Performance optimization and stability assurance
                                          • 1. Inference latency and throughput tuning
                                            • 2. Resource scheduling and fault tolerance
                                              - Inference engine and service deployment
                                              • 1. Batch processing and pipeline parallelism
                                                • 2. Edge-cloud collaborative deployment
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