Huawei H13-336_V1.0 exam - in .pdf

H13-336_V1.0 pdf
  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Aug 30, 2026
  • Q & A: 0 Questions and Answers
  • PDF Price: $888.00
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  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Aug 30, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: H13-336_V1.0
  • Exam Name: HCIE-AI Developer V1.0
  • Updated: Aug 30, 2026
  • Q & A: 0 Questions and Answers
  • Software Price: $888.00
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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Huawei AI Platform and Model Development25%-30%- ModelArts full-lifecycle development
  • 1. Model training, evaluation and selection
    • 2. Model compression, quantization and pruning
      - MindSpore framework and Ascend computing system
      • 1. Distributed training strategies
        • 2. Operator development and optimization
          Topic 2: 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
                  Topic 3: Intelligent Application Development15%-20%- Agent and multi-modal application development
                  • 1. Multi-modal fusion and service integration
                    • 2. Tool calling and task planning
                      - RAG system design and implementation
                      • 1. Prompt engineering and generation control
                        • 2. Vector database selection and retrieval optimization
                          Topic 4: 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
                                  Topic 5: 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
                                          Topic 6: 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

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