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問題 #57
A company is implementing a serverless inference API by using AWS Lambda. The API will dynamically invoke multiple AI models hosted on Amazon Bedrock. The company needs to design a solution that can switch between model providers without modifying or redeploying Lambda code in real time. The design must include safe rollout of configuration changes and validation and rollback capabilities.
Which solution will meet these requirements?
答案：A
解題說明：
Option B is the correct solution because AWS AppConfig is specifically designed to support dynamic configuration management with safe rollout, validation, and rollback, which are explicit requirements in the scenario.
By storing the active model provider configuration in AWS AppConfig, the company can switch between Amazon Bedrock model providers in real time without redeploying Lambda code. AppConfig supports deployment strategies such as canary releases, linear rollouts, and immediate deployments, allowing safe and controlled changes. If a configuration causes issues, AppConfig supports automatic rollback, reducing operational risk.
AWS AppConfig also supports schema validation, ensuring that configuration values such as model identifiers, provider names, or inference parameters are valid before being applied. This prevents misconfiguration from impacting production workloads.
Option A uses Parameter Store, which lacks native rollout strategies, validation, and automated rollback, making it unsuitable for safe real-time switching. Option C requires manual routing changes and code coupling, increasing operational overhead and deployment risk. Option D introduces unnecessary complexity by hosting configuration files in Amazon S3 when AppConfig already supports native hosted configurations.
Therefore, Option B provides the most robust, scalable, and low-maintenance solution for dynamic model switching in a serverless Amazon Bedrock inference architecture.
問題 #58
A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.
The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.
Which solution will meet these requirements?
答案：B
解題說明：
Option B best meets all stated requirements by correctly combining PII protection, evaluation before launch
, and data residency compliance using Amazon Bedrock Guardrails. Amazon Bedrock guardrails provide native sensitive information filtering that operates inline during model invocation, making them well suited for preventing personal data exposure in customer-facing AI assistants.
The requirement to evaluate how effective the AI assistant is at preventing exposure before release is best addressed by using mask mode during development and testing. Mask mode allows responses to be generated while automatically redacting detected personal information, making it easy for developers and reviewers to see where and how PII would have appeared. This provides concrete validation that the guardrail rules are correctly configured without fully blocking responses, which is ideal for quality assurance and pre- production evaluation.
For production, switching the guardrail to block mode ensures that responses containing personal information are fully prevented from being returned to users. This offers the strongest protection and aligns with compliance expectations for customer account data. Block mode is appropriate once confidence in the guardrail configuration has been established during testing.
The data residency requirement is addressed by deploying a copy of the guardrail in each AWS Region where the application operates. Amazon Bedrock guardrails are Region-specific resources, and using Region- local guardrails ensures that inference, filtering, and enforcement all occur within the same Region as the customer data. This avoids cross-Region processing and helps the company comply with regulatory and contractual data residency policies.
Option A and D incorrectly rely on cross-Region guardrails, which can violate data residency constraints.
Option C focuses on topic filtering rather than sensitive information filtering and keeps detect mode enabled in production, which does not actively prevent PII exposure. Therefore, B is the only option that fully satisfies safety, compliance, and evaluation requirements.
問題 #59
A company uses Amazon Bedrock to implement a Retrieval Augmented Generation (RAG)-based system to serve medical information to users. The company needs to compare multiple chunking strategies, evaluate the generation quality of two foundation models (FMs), and enforce quality thresholds for deployment.
Which Amazon Bedrock evaluation configuration will meet these requirements?
答案：A
解題說明：
Option B is the correct evaluation configuration because it enables end-to-end assessment of both retrieval and generation quality while supporting direct comparison of chunking strategies and foundation models.
Amazon Bedrock evaluation jobs are designed to support RAG workflows by evaluating how well retrieved context supports accurate and high-quality model outputs.
A retrieve-and-generate evaluation job evaluates the complete RAG pipeline, not just retrieval. This is essential for medical information use cases, where both the relevance of retrieved content and the correctness of generated responses directly impact user safety and trust. Including multiple chunking strategies in the evaluation dataset allows side-by-side comparison under identical prompts and conditions.
Custom precision-at-k metrics measure how effectively the retrieval component surfaces relevant chunks, while an LLM-as-a-judge metric provides qualitative scoring of generated responses. Using a numeric scale enables consistent, repeatable evaluation and supports automated quality gates. Amazon Bedrock supports LLM-based evaluators to score dimensions such as accuracy, completeness, and relevance.
Using the same evaluator model to assess outputs from both FMs ensures consistent scoring and eliminates evaluator bias. This configuration allows the company to define quantitative thresholds that must be met before deployment, enabling automated promotion through CI/CD pipelines.
Option A evaluates retrieval only and cannot assess generation quality. Option C introduces manual review, which does not scale and delays deployment. Option D separates retrieval and generation evaluation, making it harder to correlate chunking strategies with final output quality.
Therefore, Option B best meets the requirements for systematic evaluation, comparison, and quality enforcement in an Amazon Bedrock-based RAG system.
問題 #60
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?
答案：C
解題說明：
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.
問題 #61
A financial services company is using Amazon Bedrock to deploy a GenAI application across multiple business units. The company must ensure that all prompts that are used with the application ' s FMs follow regulatory compliance standards and maintain consistent formatting.
The company must implement a solution that provides version control for prompt templates, requires approval workflows for new prompts, and maintains detailed audit trails of all prompt usage and modifications.
Which combination of solutions will meet these requirements? (Select TWO.)
答案：B,E
解題說明：
Options A and C provide the most AWS-native governance approach. Amazon Bedrock Prompt Management supports reusable parameterized prompts and explicit prompt versions, allowing teams to preserve approved configurations and deploy stable versions instead of uncontrolled working drafts. An organizational approval gate can therefore be applied before a new prompt version becomes production-approved. AWS CloudTrail records Amazon Bedrock API activity, including the identity making the request, the operation performed, time, source information, and other event details, providing the required audit trail for Bedrock resource modifications and use. CloudWatch can complement this with operational usage monitoring and reporting.
Option B recreates prompt governance through S3, Lambda, and SNS. Option D uses general-purpose configuration storage rather than Bedrock-native prompt management. Option E adds substantial custom state and filtering logic without providing the same managed prompt-version lifecycle. AWS Documentation
問題 #62
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