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NEW QUESTION # 96
A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.
The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Select THREE.)
Answer: B,E,F
Explanation:
The correct combination is A, B, and D because these three options collectively satisfy the mandatory requirements for structured extraction, PII redaction before inference, regional human review, data residency, auditability, and high-scale availability with managed AWS services.
Option A is essential because Amazon Textract is the AWS-managed service designed to extract structured data from scanned documents such as forms, tables, and financial statements. Textract provides confidence scores, and Amazon Augmented AI (A2I) is purpose-built to route low-confidence extractions to human reviewers. Deploying Textract and A2I within the same Region ensures that the human review loop remains regionally constrained, meeting strict data residency requirements for applicants.
Option B satisfies the requirement to redact PII before inference by using AWS Lambda preprocessing. It also adds Amazon Bedrock guardrails to enforce safety controls on model outputs. Region-specific IAM roles ensure that only authorized principals in the correct Region can access the extracted data and invoke downstream services, strengthening residency enforcement and auditability.
Option D ensures that source documents are stored in Amazon S3 in the same Region as the applicant. Object metadata and tagging provide an auditable trail, supporting compliance reporting and traceability. S3 also provides the durability and availability needed to support 99.9% application availability as part of a well- architected pipeline.
Option C is not the correct approach for structured extraction from scans. Option E adds useful quality validation but is not strictly required to meet the stated requirements compared to A, B, and D. Option F is unrelated to the extraction/redaction/residency workflow requirements.
Therefore, A, B, and D are the best three choices to meet all stated requirements with minimal operational overhead.
NEW QUESTION # 97
A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company wants to manage these controls centrally.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: A,E
Explanation:
The correct combination is C and D because together they enforce centralized governance over both model access and prompt content controls, which are the two core requirements of the scenario.
To ensure employees can use only approved Amazon Bedrock models, governance must be enforced at the organization level and not rely on individual application logic. Service Control Policies (SCPs) are the strongest control mechanism available in AWS Organizations because they define the maximum permissions an account or principal can have. In option C, the SCP prevents any Amazon Bedrock model invocation unless a centrally approved guardrail identifier is specified. This ensures that guardrails are always enforced, regardless of how or where the invocation originates. The additional use of IAM permissions boundaries ensures that even within allowed accounts, employees are restricted to invoking only explicitly approved foundation models.
To prevent specific topics and proprietary information from being included in prompts, Amazon Bedrock Guardrails must be used. Guardrails operate inline during model invocation and can block disallowed content before it is processed by the model. Option D correctly specifies a block filtering policy, which is appropriate when content must be prevented entirely rather than partially redacted. Deploying the guardrail using AWS CloudFormation StackSets allows the company to centrally manage and consistently deploy the same guardrail configuration across all accounts in the organization, ensuring uniform enforcement.
Option E uses mask filtering, which is better suited for redacting sensitive output rather than preventing prohibited content from being submitted in prompts. Option B attempts to use SCPs alone but does not enforce guardrail deployment or content filtering. Option A incorrectly places guardrail enforcement in permissions boundaries, which are not designed to validate request parameters such as guardrail identifiers.
By combining SCP-based enforcement with centrally deployed Bedrock guardrails, options C and D together provide strong, scalable, and centrally managed controls for both content safety and model governance across the organization.
NEW QUESTION # 98
A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step- by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Option A is the best solution because it natively delivers retrieval grounding, source attribution, and low operational overhead through Amazon Bedrock Knowledge Bases. The key requirements are: retrieve from company data sources, cite sources, link claims to source documents, and keep latency under 3 seconds.
Knowledge Bases are a managed RAG capability that handles document ingestion, chunking, embeddings, retrieval, and assembly of context for model generation. This eliminates the need to build and maintain custom retrieval infrastructure.
Source attribution is crucial: the application must "link data claims to source documents." When source attribution is enabled, the RAG pipeline can return references to the underlying documents and segments used for generation. This enables traceable citations that can be surfaced to end users and used for internal auditing.
Using the Anthropic Claude Messages API (or equivalent conversational interface) with RAG allows the application to generate recommendations grounded in retrieved context while keeping responses conversational. Setting relevance thresholds helps reduce noisy retrieval, which supports both accuracy and latency targets by limiting the context passed to the model.
Storing reasoning and citations in Amazon S3 supports audit and retention needs with minimal operational burden. While the prompt may request step-by-step reasoning, AWS best practice is to produce user-facing explanations that are faithful and attributable without exposing internal reasoning traces unnecessarily. With source-grounded outputs, the system can provide concise rationale tied to citations while maintaining fast response times.
Option B emphasizes extended thinking, which increases latency and does not ensure source linkage. Option C adds significant operational overhead through custom model hosting and separate citation systems. Option D requires more custom tracking work than A while not improving retrieval attribution beyond what Knowledge Bases already provide.
Therefore, Option A best meets the requirements with the least operational overhead.
NEW QUESTION # 99
A media company is launching a platform that allows thousands of users every hour to upload images and text content. The platform uses Amazon Bedrock to process the uploaded content to generate creative compositions.
The company needs a solution to ensure that the platform does not process or produce inappropriate content.
The platform must not expose personally identifiable information (PII) in the compositions. The solution must integrate with the company's existing Amazon S3 storage workflow.
Which solution will meet these requirements with the LEAST infrastructure management overhead?
Answer: B
Explanation:
Option D is the correct solution because it relies primarily on managed, purpose-built AWS services and minimizes custom infrastructure and model management. Amazon Bedrock guardrails provide native, configurable content safety controls that can block or redact disallowed content before or after model inference. This directly ensures that the platform does not process or produce inappropriate outputs while maintaining low operational overhead.
Using Amazon Comprehend PII detection as a preprocessing step integrates cleanly with an Amazon S3- based ingestion workflow. Comprehend is a fully managed service that detects and optionally redacts PII in text without requiring custom models or pipelines. This ensures that sensitive information is removed before content is passed to Amazon Bedrock for generation.
Amazon Rekognition image moderation is purpose-built for detecting unsafe or inappropriate visual content and integrates naturally into Step Functions workflows. Step Functions provides orchestration without requiring servers or long-running infrastructure, allowing the company to integrate text and image moderation steps in a clear, auditable pipeline.
Option A introduces redundant monitoring logic and alarms that do not directly enforce content safety. Option B requires building and maintaining custom SageMaker models, increasing complexity and operational burden. Option C applies moderation at authentication time and uses services like Textract that are not designed for content moderation, increasing latency and management overhead.
Therefore, Option D best satisfies content safety, PII protection, S3 integration, and minimal infrastructure management requirements.
NEW QUESTION # 100
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: C,D
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 # 101
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