AIP-C01 · Question #84
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics…
The correct answer is B. Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock. Option B best satisfies the requirements with the least custom development effort by using native Amazon Bedrock capabilities for prompt experimentation, traffic management, fairness monitoring, and alerting. Amazon Bedrock Prompt Management allows teams to define and manage…
Question
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches. Which solution will meet these requirements with the LEAST custom development effort?
Options
- AConfigure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock
- BCreate the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock
- CSet up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon
- DCreate an Amazon Bedrock model evaluation job to compare fairness between the two prompt
How the community answered
(45 responses)- A20% (9)
- B67% (30)
- C4% (2)
- D9% (4)
Explanation
Option B best satisfies the requirements with the least custom development effort by using native Amazon Bedrock capabilities for prompt experimentation, traffic management, fairness monitoring, and alerting. Amazon Bedrock Prompt Management allows teams to define and manage multiple prompt variants without code changes, making it ideal for comparing recommendation strategies across demographic groups. Amazon Bedrock Flows enables controlled traffic allocation between prompt variants, which supports real-time A/B testing. This allows the company to collect live fairness metrics under production conditions instead of relying on offline analysis. Because Flows are fully managed, they eliminate the need for custom routing or experimentation frameworks. Amazon Bedrock guardrails provide built-in monitoring and intervention mechanisms. When configured for fairness-related checks, guardrails can detect policy violations and surface metrics such as InvocationsIntervened, which indicate when outputs are modified or blocked due to rule enforcement. These metrics integrate directly with Amazon CloudWatch, enabling real-time dashboards and threshold-based alarms. Setting an alarm at a 15% discrepancy threshold satisfies the alerting requirement with minimal configuration.
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