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HP HPE0-V31 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Operations, Management & Optimization | 25% | - Lifecycle management
- 1. Model versioning, retraining, and updating
- 2. Scaling and resource optimization
- Monitoring and observability
- 1. Logging, tracing, and alerting
- 2. Performance, resource utilization, and model health monitoring
- Troubleshooting and problem resolution
- 1. Common issues in AI infrastructure and workloads
- 2. Performance tuning and optimization techniques
|
| Security, Compliance & Business Value | 20% | - Compliance and ethics
- 1. Regulatory requirements and industry standards
- 2. Responsible AI and governance
- Business alignment and ROI
- 1. Cost management and efficiency
- 2. Measuring and demonstrating business outcomes
- Security controls
- 1. Model security and privacy
- 2. Data security, access control, and encryption
|
| AI Solutions Architecture & Design | 30% | - AI workload characteristics and requirements
- 1. Scalability, performance, and reliability considerations
- 2. Compute, memory, storage, and networking needs
- Design principles and best practices
- 1. Hybrid cloud and edge AI integration
- 2. Distributed training and inference architectures
- HPE AI portfolio and solution components
- 1. HPE storage for AI: file, object, and block storage
- 2. HPE ProLiant, Apollo, and Superdome Flex systems
- 3. HPE networking and fabric technologies
|
| Implementation & Deployment | 25% | - Integration and data preparation
- 1. Data governance and security
- 2. Data ingestion, transformation, and pipeline building
- Deployment models
- 1. Containerization and orchestration: Kubernetes, Docker
- 2. On-premises, cloud, and edge deployment
- Software and framework selection
- 1. HPE software: HPE Machine Learning Development Environment, HPE Private Cloud AI
- 2. ML frameworks: TensorFlow, PyTorch, ONNX
|