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HP HPE3-CL24 Exam Syllabus Topics:

SectionObjectives
Topic 1: HPE AI Infrastructure for Inferencing- Storage and Data Pipelines
  • 1. Low-latency storage considerations
    • 2. Data access patterns for inference workloads
      - Compute and Acceleration
      • 1. GPU-enabled systems for inference workloads
        • 2. HPE ProLiant server architecture for AI
          Topic 2: Security, Governance, and Responsible AI- Responsible AI Deployment
          • 1. Bias mitigation and monitoring
            • 2. Compliance considerations in AI systems
              - AI Security Considerations
              • 1. Data protection in inference pipelines
                • 2. Model integrity and access control
                  Topic 3: AI Foundations for Inferencing- Core AI/ML Concepts for Inference
                  • 1. Difference between training and inference workloads
                    • 2. Model lifecycle and deployment considerations
                      - AI Workload Characteristics
                      • 1. Latency, throughput, and performance constraints
                        • 2. Edge vs data center inferencing scenarios
                          Topic 4: Edge AI Inferencing Use Cases- Performance Optimization at the Edge
                          • 1. Real-time decision systems
                            • 2. Bandwidth optimization and local processing
                              - Edge Deployment Scenarios
                              • 1. Retail, manufacturing, and IoT inferencing use cases
                                • 2. On-device and near-device inference constraints
                                  Topic 5: HPE Private Cloud AI and Deployment Models- Cloud-native AI deployment
                                  • 1. HPE GreenLake AI consumption model
                                    • 2. Hybrid AI inferencing architectures
                                      - Operational Management
                                      • 1. Monitoring and optimization of inference workloads
                                        • 2. Model deployment and lifecycle operations

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