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NEW QUESTION # 34
A software company is using Amazon Q Business to build an AI assistant that allows employees to access company information and personal information by using natural language prompts. The company stores this information in an Amazon S3 bucket.
Each department in the company has a dedicated prefix in the S3 bucket. Each object name includes the S3 prefix of the department that it belongs to. Each department can belong to only a single group in AWS IAM Identity Center. Each employee belongs to a single department.
The company configures Amazon Q Business to access data stored in an S3 bucket as a data source. The company needs to ensure that the AI assistant respects access controls based on the user's IAM Identity Center group membership.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: C
Explanation:
Option B is the correct solution because Amazon Q Business natively supports access control lists (ACLs) for S3 data sources using a single, centralized JSON file that maps S3 prefixes to IAM Identity Center groups.
This approach directly aligns with the company's data organization model, where each department's data is stored under a distinct S3 prefix and each employee belongs to exactly one department group.
Using a single acl.json file at the bucket root minimizes operational overhead by centralizing access control logic in one location. Administrators can update department mappings without touching individual folders or changing IAM permissions, which simplifies governance and reduces the risk of configuration drift. Amazon Q Business automatically evaluates the user's IAM Identity Center group membership at query time and filters accessible documents accordingly.
Option A increases operational complexity by requiring a separate ACL file in every department folder, which becomes difficult to maintain as departments or prefixes change. Option C attempts to enforce access using IAM permissions sets, but Amazon Q Business access control for S3 data sources is not designed to be managed through IAM condition logic and would significantly increase complexity. Option D introduces a custom metadata structure that is not the supported mechanism for Amazon Q Business access enforcement.
Therefore, Option B provides the cleanest, most scalable, and AWS-recommended solution for enforcing department-based access control with the least operational effort.
NEW QUESTION # 35
A company is using Amazon Bedrock to build a customer-facing AI assistant that handles sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross- Region failover capabilities.
Which solution will meet these requirements?
Answer: B
Explanation:
Option A provides the most complete, AWS-native defense-in-depth solution for protecting against prompt injection attacks while meeting audit and resiliency requirements. Amazon Bedrock guardrails are designed specifically to enforce safety policies on both user inputs and model outputs, including protections against prompt injection and jailbreak attempts.
Setting content filters to high increases sensitivity to malicious or manipulative inputs. Guardrail profiles allow the same guardrail configuration to be applied consistently across multiple Regions, enabling cross- Region inference and failover without configuration drift. This directly satisfies the requirement for regional resilience.
Amazon CloudWatch Logs captures detailed guardrail intervention events, including when content is blocked, modified, or flagged. Custom metrics derived from these logs enable fine-grained auditing, alerting, and reporting on safety enforcement actions. This provides a more detailed audit trail of safety interventions than API-level logs alone.
Option B adds WAF protection but lacks detailed guardrail intervention logging. Option C introduces additional services and custom logic that increase complexity and may miss model-specific injection patterns.
Option D references replication concepts that are not aligned with Bedrock guardrail operational models and relies on word filters, which are insufficient against sophisticated prompt injection techniques.
Therefore, Option A best meets the requirements for layered protection, auditability, and cross-Region resilience using managed Amazon Bedrock safety controls.
NEW QUESTION # 36
A university is building an AI-powered application that includes several sub-applications. The sub- applications include AI assistants, assignment graders, and internal analytics applications. The university is defining and testing multiple prompts by using various foundation models (FMs). The university wants to compare variants of each prompt and choose the variant that yield outputs that are best-suited for specified use cases. The university requires a version control solution for the prompts. The university must be able to test prompt variations and collect audit trails for prompt changes and usage. The solution must also maintain consistency while allowing the prompts to integrate into the main application. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: B,E
Explanation:
Amazon Bedrock Prompt Management is the purpose-built service for prompt lifecycle management. it provides native version control , allowing developers to test and compare variants side-by-side. Use of parameterized variables ensures that a single prompt structure can be consistently reused across different sub-applications (assistants vs. graders) while still being tailored to the specific context. To " integrate into the main application " with minimal overhead, Amazon Bedrock Flows provide a managed orchestration layer.
Flows allow developers to link managed prompts with AWS services (like knowledge bases or Lambda functions) without writing complex state-machine logic in Step Functions (Option B). This combination ensures consistent, auditable, and easily deployable prompt assets across the university ' s diverse use cases.
NEW QUESTION # 37
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: C
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 # 38
A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real- time personally identifiable information (PII) filtering and audit trails to automatically report compliance.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is the most comprehensive and architecturally aligned solution for meeting end-to-end data lineage, real-time PII filtering, and automated compliance reporting requirements in a medical GenAI system built on Amazon Bedrock. Each requirement maps directly to a managed AWS service that is purpose-built for governance, security, and compliance.
AWS Glue Data Catalog is designed to register datasets across multiple sources and maintain metadata that supports lineage tracking. By cataloging all inputs that flow into the Bedrock-based system, the organization can trace how data moves from ingestion through processing and storage, which is essential for regulatory audits in healthcare environments.
For real-time PII filtering, Amazon Bedrock Guardrails provide native PII detection and filtering during model inference. Guardrails operate inline with model invocation, ensuring sensitive information is blocked or redacted before responses are returned to users. This satisfies the requirement for real-time protection rather than post-processing analysis.
AWS CloudTrail delivers a complete audit trail of all Amazon Bedrock API calls, including InvokeModel requests and configuration changes. Storing these logs in Amazon S3 enables long-term retention and supports compliance audits. CloudTrail ensures traceability of who accessed the system, when, and how it was used.
To strengthen compliance monitoring, Amazon Macie continuously scans stored data for sensitive information and automatically classifies findings. Publishing Macie findings to Amazon CloudWatch Logs and visualizing them through dashboards enables near-real-time visibility into compliance posture and supports automated reporting workflows.
The other options fall short. Option B performs PII filtering at the application edge rather than at inference time and relies on scheduled analysis instead of real-time enforcement. Option C focuses on replication and document processing rather than inline GenAI governance. Option D uses services that are not designed for PII detection in text-based GenAI workflows and lacks native lineage tracking.
Therefore, A best fulfills all stated requirements using AWS-recommended governance and security capabilities.
NEW QUESTION # 39
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