Option C is the best answer because the scenario is centered on a foundation-model GenAI application that needs explainability, fairness iteration, and responsible AI controls inside an Amazon Bedrock workflow. Amazon Bedrock Agents support tracing, and AWS documentation states that traces can show the agent’s path from user input to response, including action group inputs and outputs, knowledge base queries, and the reasoning the agent uses to decide which action or query to take. AWS also describes trace data as a way to understand how an agent arrived at a response. This directly addresses clinicians’ need to review diagnostic reasoning and decision flow.

Amazon Bedrock Guardrails are also the right service for policy compliance in GenAI applications. AWS documentation states that Guardrails can implement safeguards aligned with responsible AI policies, configure denied topics, filter harmful content, and remove sensitive information. For healthcare and life sciences GenAI use cases, AWS Prescriptive Guidance recommends evaluating bias, fairness, and hallucinations and implementing guardrails to prevent harmful responses. This supports strict governance and privacy-sensitive clinical workflows.

The prompt testing and A/B testing component supports rapid iteration. AWS guidance for generative AI operations recommends prompt template management, creating and testing prompt variants, using A/B testing workflows for prompt variants, and analyzing performance against metrics. This is relevant for testing fairness behavior across demographic prompt sets and clinical scenarios.

Option A includes SageMaker Clarify, which is useful for bias detection and model explainability, but it is less directly aligned to Bedrock-native real-time tracing and policy enforcement for an FM application. Option B handles medical text extraction and terminology but not reasoning transparency or governance. Option D provides operational metrics and custom reports, but not native Bedrock reasoning traces or guardrails. Therefore, option C best meets the GenAI governance requirements.

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AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for " tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.

The correct answers are B and C because they directly address hallucination reduction while maintaining high throughput and low latency.

Option B reduces hallucinations at their source by grounding model outputs in verified content through Retrieval Augmented Generation (RAG). Using an Amazon Bedrock knowledge base with semantic chunking ensures that long technical documents are broken into meaningfully coherent sections. This allows the model to retrieve only the most relevant chunks, rather than processing an entire document in one pass, which significantly improves factual accuracy and reduces cognitive overload on the model. This approach scales efficiently and supports processing more than 1,000 documents per hour.

Option C adds a defense-in-depth safety layer by using Amazon Bedrock guardrails to detect and block hallucination-like output patterns. Guardrails operate at inference time with minimal performance overhead, making them suitable for low-latency requirements. While guardrails do not eliminate hallucinations entirely, they effectively prevent unsafe or clearly fabricated outputs from reaching users.

Option A increases latency and cost due to explicit reasoning steps and does not scale well for high-throughput workloads. Option D increases randomness and worsens hallucinations. Option E repeats the existing flawed approach.

Therefore, Options B and C together provide scalable grounding and runtime protection that meet accuracy, performance, and throughput requirements.