A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual…

Question

Options

• AConfigure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount
• BRun Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations.
• CConfigure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text
• DUse AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon

• A
  19% (5)
• B
  12% (3)
• C
  65% (17)
• D
  4% (1)

Explanation

Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development. By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation. Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines. Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.

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