Huawei HCIP-AI-Ascend Developer V2.0 : H13-324_V2.0

Huawei H13-324_V2.0 Actual PDF
  • Exam Code: H13-324_V2.0
  • Exam Name: HCIP-AI-Ascend Developer V2.0
  • Updated: Sep 04, 2026
  • Q & A: 0 Questions and Answers

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Huawei H13-324_V2.0 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Neural Network Basics25%- Common neural network structures and algorithms
- Mathematical foundation for AI development
- Model training, optimization and evaluation
- Basic principles of deep learning
Topic 2: Comprehensive Experiment and Application Practice5%- End-to-end AI application development based on Ascend
- Problem analysis and solution design
- Deployment and testing of actual projects
Topic 3: CANN - Custom Operator Development8%- Operator performance analysis and tuning
- Development, compilation and deployment of custom operators
- Operator development framework and specifications
Topic 4: CANN - ATC Model Conversion8%- CANN architecture and functions
- Model optimization and accuracy comparison
- ATC tool usage and parameter configuration
- Conversion from mainstream frameworks to Ascend OM model
Topic 5: CANN - Inference Application Development Process8%- Inference engine and API introduction
- Data preprocessing and postprocessing
- Application development workflow
- Multi-thread and batch processing design
Topic 6: MindSpore Full-Process Development20%- Model saving, loading and deployment
- MindSpore framework features and installation
- Automatic differentiation and parallel computing
- Network construction, training and inference
Topic 7: Ascend AI Processor8%- Hardware specifications and computing resources
- Memory management and data flow
- Architecture and features of Ascend AI processor
Topic 8: Atlas AI Computing Solution13%- Atlas hardware product series and application scenarios
- System composition and deployment modes
- Software stack and development environment
Topic 9: CANN - Inference Application Performance Tuning5%- Data transmission and computing efficiency improvement
- Performance indicators and analysis methods
- System configuration and resource optimization

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