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29. Frage
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option C is the correct solution because it enforces privacy controls at inference time, not at ingestion time, which is required when different user roles require different visibility into the same underlying data.
Using an S3 Lifecycle configuration ensures that documents older than 3 years are automatically removed, guaranteeing that the knowledge base references only compliant, recent medical reports. Scheduling Lambda- based syncs keeps the knowledge base aligned with the bucket contents without introducing complex per- upload orchestration.
The most important requirement is role-based PII exposure. Amazon Bedrock guardrails support dynamic application at inference time, allowing the system to select a guardrail configuration based on the authenticated user's Amazon Cognito group. Surgeons can receive full responses, while engineers receive responses with PII masked-without duplicating data or maintaining multiple knowledge bases.
This approach preserves a single source of truth for medical reports while enforcing privacy through response- level controls. It also maintains full auditability of access and redaction behavior.
Option A permanently removes PII and violates surgeon access requirements. Option B redacts data inconsistently and couples privacy logic to ingestion. Option D doubles storage, increases cost, and introduces data drift risk.
Therefore, Option C best meets privacy, compliance, scalability, and operational efficiency requirements.
30. Frage
A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions.
The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option D is correct because it separates the United States and European workloads and uses the correct Amazon Bedrock inference pattern for European data residency. AWS documentation states that geographic cross-Region inference keeps data processing within specified geographic boundaries such as US, EU, and APAC, and is intended for organizations with data residency and compliance requirements. The regional availability documentation further explains that a geographic profile routes within a defined geography and that prompts and outputs may move within that geography but not outside it. This directly satisfies the requirement that European customer data must be processed only in Europe-based AWS Regions.
Provisioned throughput is appropriate for the US workload because the system has a demanding and predictable performance target: 1,000 transactions per second with sub-500 ms response times. Provisioned capacity is generally used when an application needs reserved throughput and consistent performance rather than depending only on on-demand capacity. Automatic scaling based on utilization metrics supports changing demand while maintaining performance. EventBridge and Lambda can coordinate failover workflows or routing updates when connectivity disruptions occur, supporting high availability across Regions.
Option A is not the best answer because it includes a global inference profile. AWS documentation states that global cross-Region inference can route requests across supported commercial AWS Regions worldwide and has no geographic data-residency restriction. AWS explicitly notes that organizations with data residency or compliance requirements should assess whether global cross-Region inference fits their framework because requests may be processed in other supported commercial Regions. That conflicts with the European data processing requirement.
Option B uses separate Regional provisioned throughput, but it relies on a custom Application Load Balancer approach for Bedrock routing and does not use the Bedrock geographic inference profile that is designed for EU data residency. Option C uses response streaming, which improves perceived response latency for text generation but does not guarantee sub-500 ms fraud-processing latency or European-only processing.
Therefore, option D is the most complete AWS-aligned solution.
31. Frage
A company deploys multiple Amazon Bedrock-based generative AI (GenAI) applications across multiple business units for customer service, content generation, and document analysis. Some applications show unpredictable token consumption patterns. The company requires a comprehensive observability solution that provides real-time visibility into token usage patterns across multiple models. The observability solution must support custom dashboards for multiple stakeholder groups and provide alerting capabilities for token consumption across all the foundation models that the company's applications use.
Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Antwort: B,C
Begründung:
The combination of Options C and D delivers comprehensive, real-time observability for Amazon Bedrock workloads with the least operational overhead by relying on native integrations and managed services.
Amazon Bedrock publishes built-in CloudWatch metrics for model invocations and token usage. Option C leverages these native metrics directly, allowing teams to build centralized CloudWatch dashboards without additional data pipelines or custom processing. CloudWatch alarms provide threshold-based alerting for token consumption, enabling proactive cost and usage control across all foundation models. This approach aligns with AWS guidance to use native service metrics whenever possible to reduce operational complexity.
Option D complements CloudWatch by enabling advanced, stakeholder-specific visualizations through Amazon Managed Grafana. The zero-ETL integration allows Bedrock and CloudWatch metrics to be visualized directly in Grafana without building ingestion pipelines or managing storage layers. Grafana dashboards are particularly well suited for serving different audiences, such as engineering, finance, and product teams, each with customized views of token usage and trends.
Option A introduces unnecessary complexity by adding a business intelligence layer that is better suited for historical analytics than real-time operational monitoring. Option B is useful for deep log analysis but requires query maintenance and does not provide efficient real-time dashboards at scale. Option E involves multiple services and custom data flows, significantly increasing operational overhead compared to native metric- based observability.
By combining CloudWatch dashboards and alarms with Managed Grafana's zero-ETL visualization capabilities, the company achieves real-time visibility, flexible dashboards, and automated alerting across all Amazon Bedrock foundation models with minimal operational effort.
32. Frage
A bank is developing a generative AI (GenAI)-powered AI assistant that uses Amazon Bedrock to assist the bank's website users with account inquiries and financial guidance. The bank must ensure that the AI assistant does not reveal any personally identifiable information (PII) in customer interactions.
The AI assistant must not send PII in prompts to the GenAI model. The AI assistant must not respond to customer requests to provide investment advice. The bank must collect audit logs of all customer interactions, including any images or documents that are transmitted during customer interactions.
Which solution will meet these requirements with the LEAST operational effort?
Antwort: D
Begründung:
Option C is the correct solution because Amazon Bedrock guardrails are purpose-built to enforce defense-in- depth safety controls for GenAI applications with minimal operational overhead. Guardrails provide managed, policy-based enforcement that operates before prompts are sent to the foundation model and after responses are generated, which directly satisfies the requirement that PII must not be sent to the model and must not appear in outputs.
By configuring a sensitive information policy, the application can automatically detect and redact PII in user inputs and model responses without building custom preprocessing pipelines. This approach is more reliable and scalable than regex or prompt engineering techniques, which are brittle and error-prone for sensitive data handling.
The topic policy capability in Amazon Bedrock guardrails allows the bank to explicitly block investment advice topics, ensuring regulatory compliance. This policy-based approach is safer and more auditable than attempting to steer the model only through prompt instructions.
Using the Converse API enables structured, standardized interactions with the model and supports consistent logging of requests and responses. Enabling delivery logging and image logging to Amazon S3 ensures that all customer interactions, including documents and images, are captured in a durable, auditable storage layer.
This directly supports compliance, regulatory audits, and forensic analysis.
Option A incorrectly relies on Amazon Macie, which is designed for data-at-rest discovery rather than real- time conversational filtering. Option B introduces custom Lambda pipelines and topic modeling, increasing operational complexity. Option D relies on regex and prompt engineering, which do not meet financial-grade compliance standards.
Therefore, Option C delivers the strongest security, governance, and auditability with the least operational effort.
33. Frage
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?
Antwort: C
Begründung:
AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for
" tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.
34. Frage
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