AIP-C01 · Question #56
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block…
The correct answer is C. Configure a guardrail and set content filters to medium for harmful content. Set up denied topics. Option C is the correct solution because it uses a single, well-tuned Amazon Bedrock guardrail that applies different actions to different content types, which is the recommended approach for minimizing false positives while enforcing strong policy controls. Setting content…
Question
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity. The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content. Which solution will meet these requirements?
Options
- AConfigure a single guardrail and set content filters to high for all categories. Set up denied topics
- BConfigure multiple guardrails by using tiered policies. Create one guardrail and set content filters
- CConfigure a guardrail and set content filters to medium for harmful content. Set up denied topics
- DCreate a separate guardrail for each use case. Create one guardrail that applies a harmful
How the community answered
(17 responses)- A12% (2)
- C82% (14)
- D6% (1)
Explanation
Option C is the correct solution because it uses a single, well-tuned Amazon Bedrock guardrail that applies different actions to different content types, which is the recommended approach for minimizing false positives while enforcing strong policy controls. Setting content filters to medium rather than high reduces overblocking of benign customer conversations while still preventing harmful content. Amazon Bedrock guardrails are designed to balance precision and recall, and medium sensitivity is commonly recommended for customer- facing financial services use cases. Denied topics explicitly prevent the assistant from discussing investment advice, which is a regulatory requirement. Including definitions and sample phrases improves detection accuracy and reduces ambiguity. Sensitive information filters support different actions per context. Masking PII in responses preserves conversational usefulness for legitimate customer support while preventing exposure of sensitive data. Blocking sensitive financial information in inputs prevents downstream processing of disallowed content before it reaches the foundation model. Critically, enabling both input and output evaluation ensures that guardrails are applied consistently at every stage of interaction. Custom blocked messages and audit logging provide clear compliance evidence for regulators and internal audits.
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