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: Sep 20, 2026

Q & A: 0 Questions and Answers

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Huawei H13-336_V1.0 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Security and Governance5%-10%- Model security and privacy protection
  • 1. Adversarial attack and defense
    • 2. Data desensitization and federated learning
      - Compliance and lifecycle governance
      • 1. Model audit and interpretability
        • 2. Compliance with AI industry regulations
          Topic 2: 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
                  Topic 3: 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. Data quality assessment and governance
                        • 2. Large-scale dataset construction
                          Topic 4: 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
                                  Topic 5: 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
                                          Topic 6: 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

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