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26. Frage
A financial services company is deploying a generative AI (GenAI) application that uses Amazon Bedrock to assist customer service representatives to provide personalized investment advice to customers. The company must implement a comprehensive governance solution that follows responsible AI practices and meets regulatory requirements.
The solution must detect and prevent hallucinations in recommendations. The solution must have safety controls for customer interactions. The solution must also monitor model behavior drift in real time and maintain audit trails of all prompt-response pairs for regulatory review. The company must deploy the solution within 60 days. The solution must integrate with the company's existing compliance dashboard and respond to customers within 200 ms.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: D
Begründung:
Option A is the correct solution because it uses native Amazon Bedrock governance and evaluation capabilities to meet regulatory, performance, and deployment timeline requirements with the least operational overhead.
Amazon Bedrock guardrails provide built-in safety controls that enforce responsible AI policies directly during inference. Custom content filters and toxicity detection protect customer interactions and prevent disallowed investment guidance patterns without requiring custom application logic. Guardrails operate inline and are optimized for low latency, which helps meet the strict 200 ms response-time requirement.
Hallucination detection is addressed through Amazon Bedrock Model Evaluation, which supports automated evaluation at scale using LLM-as-a-judge techniques. This enables the company to detect factual inaccuracies and policy violations systematically, without building custom evaluation pipelines or requiring extensive human review. Evaluation outputs can be surfaced as metrics.
Storing all prompt-response pairs in Amazon DynamoDB provides a low-latency, highly scalable audit store that aligns with financial regulatory requirements. Using TTL enforces data retention policies automatically, reducing compliance risk and storage overhead.
Amazon CloudWatch custom metrics integrate seamlessly with existing compliance dashboards, allowing near-real-time monitoring of safety interventions, hallucination rates, and drift indicators. CloudWatch anomaly detection can be applied to these metrics to surface behavior changes quickly.
Option B relies on custom Lambda logic and S3-based auditing, increasing latency and operational complexity. Option C introduces additional services that increase setup time and may exceed the 60-day deployment window. Option D uses non-Bedrock-native monitoring and adds unnecessary infrastructure layers.
Therefore, Option A provides the most complete, compliant, and low-overhead governance solution for a regulated GenAI financial services application.
27. Frage
A logistics company is building an agentic GenAI-powered solution to automate freight optimization. The solution must retrieve data in real time from multiple internal and external systems. The solution must include a human-in-the-loop approval step before the optimization process is finished. The solution must support modular growth as the number of integrations and amount of logic increases.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: D
Begründung:
Option B provides the strongest modular architecture for an integration-heavy agentic workload. Strands supports hierarchical agents-as-tools patterns in which a coordinating agent delegates specialized work to independent agents, allowing integrations and domain logic to be added or modified without creating one increasingly complex agent. AgentCore Gateway exposes tools through standardized MCP interfaces so agents can discover and invoke internal or external capabilities through a unified connectivity layer. AWS Step Functions supports callback tasks that pause execution until a human or external process returns a task token, making it suitable for the required human approval checkpoint. Option A requires custom model hosting and approval UI development. Option C is primarily a batch data-processing architecture rather than real-time agent orchestration. Option D can function initially, but a single agent becomes less modular and maintainable as the number of tools and business domains expands. Strands Agents
28. Frage
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?
Antwort: A
Begründung:
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.
29. Frage
A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM.
Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.
Which combination of solutions will meet this requirement? (Select TWO.)
Antwort: B,C
Begründung:
The correct answers are A and D because they directly reduce time-to-first-token and stabilize p95 latency for interactive, real-time chat workloads hosted on Amazon SageMaker AI real-time endpoints.
Option D addresses the biggest driver of uneven latency: cold starts and scale-to-zero behavior. By setting the minimum number of instances to greater than 0, the endpoint always has warm capacity and loaded runtime resources, eliminating the first-request penalty that causes users to wait multiple seconds. Enabling response streaming improves perceived latency by returning the first tokens as soon as they are generated rather than waiting for the complete response. This directly targets the abandonment problem described (users leaving after waiting for the first token).
Option A further improves p95 latency and throughput by removing model loading overhead during inference and improving GPU utilization. Preloading model weights during container startup ensures the model is ready before traffic arrives and avoids unpredictable on-demand weight loading. Dynamic batching increases efficiency by grouping compatible requests into a single inference pass, reducing per-request overhead and improving GPU saturation. When tuned properly for interactive workloads, batching can reduce tail latency while preserving responsiveness by enforcing small batch windows.
Option B makes latency worse because setting minimum instances to 0 and lazily loading weights guarantees cold-start delays and unpredictable first-token performance. Option C similarly increases cold-start behavior through lazy loading and offers no batching benefits. Option E is designed for non-interactive workloads and introduces queueing and storage latency, which conflicts with the 800 ms p95 requirement for interactive chat.
Therefore, A and D are the best combination to achieve consistently low p95 latency and fast first-token streaming for a SageMaker-hosted chat assistant.
30. Frage
A global financial services company hosts a fraud-alert system that uses an Amazon Bedrock foundation model (FM) to generate explanations for suspicious transactions. The company processes regulated financial data across three geographic areas. The system must maintain consistent responsiveness globally, support multi-Region failover, and provide full observability for audit and compliance teams.
Load testing shows that the FM's total inference time cannot be reduced. The company cannot increase its inference costs, change the FM, modify token counts, or provision additional compute capacity. Users report that the UI performs slowly because it waits for the complete model response before it shows any content.
The company must improve perceived responsiveness during peak periods, when the system can receive
10,000-15,000 concurrent requests. The solution must maintain multi-Region resiliency and full monitoring visibility.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option A is correct because the problem is not total model runtime; the problem is perceived UI responsiveness. AWS documentation states that the Amazon Bedrock InvokeModelWithResponseStream API returns the model response as a stream. AWS also explains that streaming allows an application to access responses in chunks without waiting for the entire result. This lets the frontend display generated content as tokens or chunks arrive, improving the user experience even when the total inference duration remains unchanged.
This option also preserves the stated constraints. It does not require changing the foundation model, increasing token counts, provisioning extra compute, or reducing the model's actual inference time. Instead, it changes how the response is delivered to the user. This is exactly the right pattern when users perceive slowness because the interface waits for the full completion before rendering anything. AWS examples for Bedrock streaming show applications processing response streams in real time, which supports interactive applications during high-concurrency periods.
The monitoring portion of option A is also aligned with AWS guidance. Amazon Bedrock runtime publishes CloudWatch metrics for invocation volume, latency, token consumption, error rates, and model invocation logging delivery. CloudWatch GenAI observability can track invocation count, token usage, and errors using built-in views. This supports audit and compliance visibility across Regions.
Option B adds caching of partially processed fraud explanations, which can be risky for regulated transaction- specific explanations and does not solve the UI issue for unique requests. Option C changes inputs by condensing them, which violates the constraint not to modify token counts or inputs. Option D focuses on Lambda concurrency and log retention, which does not address waiting for the full model response.
Therefore, response streaming with CloudWatch and tracing is the correct solution.
31. Frage
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