[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"prev-aws-certified-ai-practitioner-genai-fundamentals-genai-business-value-selecting-a-genai-model-en":4,"lesson-aws-certified-ai-practitioner-genai-fundamentals-genai-business-value-selecting-a-genai-model-en":19,"next-aws-certified-ai-practitioner-genai-fundamentals-genai-business-value-selecting-a-genai-model-en":30},null,{"locked":5,"reason":6,"meta":7,"item":3},true,"paywall",{"title":8,"description":9,"isFree":10,"estimatedMinutes":11,"difficulty":12,"learningObjectives":13},"Limitations and Risks of Generative AI","Hallucination, inaccuracy, nondeterminism, and interpretability are four different failures with four different fixes. Learn to tell them apart, and see how Amazon Bedrock detects the one you cannot prevent.",false,18,"beginner",[14,15,16,17,18],"Identify the disadvantages of generative AI named in the exam guide: hallucinations, interpretability, inaccuracy, and nondeterminism","Distinguish a hallucination from a plain inaccuracy, and both from output variation","Explain how temperature, top-p, and top-k produce nondeterministic output and what lowering them does","Describe how the Amazon Bedrock Guardrails contextual grounding check scores grounding and relevance","Match each failure mode to the mitigation that actually addresses it",{"locked":5,"reason":6,"meta":20,"item":3},{"title":21,"description":22,"isFree":10,"estimatedMinutes":11,"difficulty":23,"learningObjectives":24},"Selecting a Generative AI Model","Turn the exam's list of selection factors into a working order of operations: eliminate on hard constraints, evaluate the survivors on your own task, then optimize for cost and latency.","intermediate",[25,26,27,28,29],"Identify the factors the exam names for selecting a generative AI model, and the additional factors AWS lists in the model selection lifecycle stage","Apply the three gates in order: hard constraints, capability on your task, then cost and latency","Compare the evaluation options in Amazon Bedrock: programmatic, judge model, and human","Explain why the largest model is usually the wrong default and what model complexity actually costs","Describe why model selection is reversible on Bedrock and what must be redone after a switch",{"locked":5,"reason":6,"meta":31,"item":3},{"title":32,"description":33,"isFree":10,"estimatedMinutes":11,"difficulty":23,"learningObjectives":34},"Measuring Generative AI Business Value","Model scores do not prove business value. Work through the metrics the exam names, from accuracy and cross-domain performance up to ROI, conversion rate, and customer lifetime value, with a full ROI calculation on a real workload.",[35,36,37,38,39],"Identify the business value metrics the exam names for generative AI applications","Separate model-level metrics from operational metrics and business metrics, and explain why a high score at one level proves nothing at the level above","Define conversion rate, average revenue per user, and customer lifetime value, and name the generative AI lever that moves each one","Calculate return on investment for a generative AI application from token cost, build cost, and avoided labor","Explain why a baseline measured before launch is what makes any value claim defensible"]