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HP HPE3-CL24 Exam Syllabus Topics:
| Section | Objectives |
| Security, Governance, and Responsible AI | - Responsible AI Deployment
- 1. Bias mitigation and monitoring
- 2. Compliance considerations in AI systems
- AI Security Considerations
- 1. Model integrity and access control
- 2. Data protection in inference pipelines
|
| HPE Private Cloud AI and Deployment Models | - Cloud-native AI deployment
- 1. Hybrid AI inferencing architectures
- 2. HPE GreenLake AI consumption model
- Operational Management
- 1. Model deployment and lifecycle operations
- 2. Monitoring and optimization of inference workloads
|
| HPE AI Infrastructure for Inferencing | - Compute and Acceleration
- 1. GPU-enabled systems for inference workloads
- 2. HPE ProLiant server architecture for AI
- Storage and Data Pipelines
- 1. Low-latency storage considerations
- 2. Data access patterns for inference workloads
|
| Edge AI Inferencing Use Cases | - Performance Optimization at the Edge
- 1. Bandwidth optimization and local processing
- 2. Real-time decision systems
- Edge Deployment Scenarios
- 1. Retail, manufacturing, and IoT inferencing use cases
- 2. On-device and near-device inference constraints
|
| AI Foundations for Inferencing | - Core AI/ML Concepts for Inference
- 1. Model lifecycle and deployment considerations
- 2. Difference between training and inference workloads
- AI Workload Characteristics
- 1. Edge vs data center inferencing scenarios
- 2. Latency, throughput, and performance constraints
|