Exam Prep, LLM Optimization & Evaluation

This culminating unit prepares students for the AWS Certified AI Practitioner exam by reviewing exam essentials, reinforcing core concepts, and practicing with targeted review questions. It focuses on inference optimizations for large language models, hands‑on evaluation using Amazon Bedrock, and assessing the benefits and risks of generative AI to support informed deployment decisions.

Learning Objectives

• Analyze inference optimization techniques for large language models (e.g., quantization, distillation, batching, caching, model selection) and compare their impacts on latency, cost, and model accuracy
• Apply Amazon Bedrock evaluation methods to design and execute model assessments, interpret evaluation metrics, and choose appropriate model options based on empirical results
• Demonstrate exam readiness by completing practice exam essentials and review question sets, identifying knowledge gaps, and producing a prioritized remediation study plan
• Evaluate the upsides and downsides of generative AI (including bias, hallucination, privacy, and security concerns) and recommend mitigation and risk‑management strategies for responsible deployment