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NEW QUESTION # 33
A healthcare company is developing an Amazon Bedrock-based AI clinical assistant. The company needs a solution to track model inputs and outputs so the company can perform offline quality evaluation and regression analysis. The solution must provide centralized, immutable audit records of all model inference and decision events. The company needs a solution that requires minimal runtime overhead so interactive latency remains stable during evaluation and auditing.
Which solution will meet these requirements?
Answer: A
Explanation:
Option C is correct because it combines complete inference capture, immutable retention, and offline evaluation without placing an evaluator in the synchronous request path. Amazon Bedrock model invocation logging can record request data, response data, and metadata to CloudWatch Logs and Amazon S3. S3 Object Lock provides write-once-read-many protection that can prevent retained audit objects from being overwritten or deleted. Bedrock model evaluation jobs can then assess stored or prepared response data offline, supporting regression and quality analysis without adding judge-model latency to every live clinical request. CloudTrail complements the audit record for service API activity. Option A depends on manual reviews. Option B records API activity but not the full inference payloads needed for quality evaluation, and ordinary versioning is not equivalent to WORM retention. Option D scores every request in production, adding runtime overhead.
AWS Documentation
NEW QUESTION # 34
A financial services company uses Amazon Bedrock to analyze customer data that is stored in an Amazon S3 bucket. The data includes personally identifiable information (PII). The company must mask PII from foundation model (FM) responses.
Which solution will meet this requirement with the LEAST operational effort?
Answer: A
Explanation:
Option A is correct because Amazon Bedrock Guardrails provides a native sensitive-information filter for personally identifiable information. The filter can detect supported PII types and use the MASK action so detected values in model requests or responses are replaced with placeholders such as the PII type. This directly satisfies the requirement to mask PII from foundation model responses with minimal operational effort. It avoids creating and maintaining an additional preprocessing pipeline. Option B can detect or redact PII with Amazon Comprehend, but it requires Lambda logic and data-processing workflows before Bedrock invocation. Options C and D require scanning, copying, and segregating objects across S3 buckets and still do not directly enforce response masking. For response-level protection in Bedrock, the managed Guardrails capability is the simplest and most purpose-built control. AWS Documentation
NEW QUESTION # 35
A medical company uses Amazon Bedrock to power a clinical documentation summarization system. The system produces inconsistent summaries when handling complex clinical documents. The system performed well on simple clinical documents.
The company needs a solution that diagnoses inconsistencies, compares prompt performance against established metrics, and maintains historical records of prompt versions.
Which solution will meet these requirements?
Answer: B
Explanation:
Option B best meets the requirements because it provides systematic diagnosis, measurable comparison, and historical traceability of prompt performance. By placing prompts under version control and testing them against complex clinical documents, the company can consistently reproduce issues, track regressions, and compare prompt behavior using quantifiable metrics such as factual accuracy, completeness, and consistency.
Automated testing ensures scalability and repeatability, while version history preserves prompt evolution over time.
Option A lacks objective metrics and does not address complex documents. Option C focuses on live traffic experimentation but does not inherently diagnose prompt inconsistencies or preserve detailed historical evaluations. Option D adds medical entity analysis but introduces unnecessary service coupling and does not provide robust prompt version history or automated comparative benchmarking. Therefore, Option B is the most complete and disciplined solution.
NEW QUESTION # 36
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs).
A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear, it does not always flow with the rest of the text.
The GenAI developer must ensure that the promotional message always appears in the FM responses.
Which solution will meet this requirement?
Answer: B
Explanation:
Option D is the intended Bedrock prompt-engineering solution. AWS guidance recommends using output indicators to explicitly describe constraints and characteristics that the model should follow when constructing its response. The prompt should clearly state that the promotional message is mandatory, where it should appear, and how it should be integrated with the surrounding recommendation. This gives the model a stronger output constraint than an indirect instruction embedded elsewhere in a dynamically assembled prompt. Option A uses Guardrails, which are designed to evaluate and constrain unsafe or disallowed content rather than require insertion of specific marketing text. Option B adds unnecessary model calls and still does not enforce the desired response structure. Option C introduces additional AgentCore and reranking processing for a requirement that should be addressed directly through explicit output instructions in the prompt. AWS Documentation
NEW QUESTION # 37
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company's approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)
Answer: B,E,F
Explanation:
The correct combination is A, D, and F because these guardrail features directly map to the stated financial compliance and governance requirements.
Option A is required because denied topics guardrails are explicitly designed to block entire categories of requests, such as requests for guaranteed returns or specific stock recommendations. These are regulatory- sensitive scenarios where partial filtering is insufficient and full blocking is required to prevent violations.
Option D is correct because custom word filters are the appropriate guardrail mechanism to block references to specific competitor names. Content filters are category-based (such as hate, sexual, or violence-related content) and are not suitable for blocking organization-specific competitor references. Custom word filters allow precise blocking at both input and output stages.
Option F is required because a high grounding score threshold enforces that model outputs must be strongly supported by approved source material. This prevents the AI assistant from making speculative or unfounded claims that are not aligned with the company's approved financial guidance, which is critical in regulated financial environments.
Option B is incorrect because content filters do not target domain-specific financial advice patterns. Option C is incorrect for the same reason-competitor names are not a content filter category. Option E would weaken factual grounding and increase hallucination risk.
Therefore, A, D, and F together provide topic blocking, competitor exclusion, and factual grounding enforcement.
NEW QUESTION # 38
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