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Answer: Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
## Explanation **Option B is correct** because it leverages Amazon Bedrock's built-in capabilities with minimal custom development: 1. **Amazon Bedrock Prompt Management** - Provides native support for creating and managing prompt variants 2. **Amazon Bedrock Flows** - Offers built-in deployment capabilities with traffic allocation 3. **Amazon Bedrock Guardrails** - Includes native fairness monitoring features 4. **CloudWatch alarms on GuardrailContentSource dimension** - Uses existing Bedrock metrics without custom code **Why other options require more custom development:** - **Option A**: Requires custom Lambda functions for post-processing analysis and custom metric creation - **Option C**: Requires integration with SageMaker Clarify and custom composite alarm configuration - **Option D**: Requires model evaluation jobs and custom dimension-based alarm setup **Key AWS Services Used in Option B:** - **Amazon Bedrock Prompt Management**: For prompt variant creation - **Amazon Bedrock Flows**: For deployment with traffic allocation - **Amazon Bedrock Guardrails**: For fairness monitoring - **Amazon CloudWatch**: For monitoring and alerting This solution minimizes custom development by using AWS managed services with built-in fairness monitoring capabilities.
Author: Ducse Chen
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Which solution will meet these requirements with the LEAST custom development effort?
A
Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS Lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.
B
Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
C
Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics.
D
Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.