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HP HPE3-CL24 Exam Syllabus Topics:

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

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