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HP HPE3-CL24 Exam Syllabus Topics:

Section	Objectives
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. Real-time decision systems 2. Bandwidth optimization and local processing
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. Monitoring and optimization of inference workloads 2. Model deployment and lifecycle operations
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
HPE AI Infrastructure for Inferencing	- Storage and Data Pipelines 1. Data access patterns for inference workloads 2. Low-latency storage considerations - Compute and Acceleration 1. HPE ProLiant server architecture for AI 2. GPU-enabled systems for inference workloads
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