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NEW QUESTION # 121
A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture.
The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is the most appropriate design because it provides scalable multi-agent orchestration, clear domain separation, and strong governance with minimal operational complexity. A supervisor-agent pattern is a standard AWS-recommended approach for multi-agent systems: one agent performs intent classification and routing, while specialized agents handle domain-specific tasks.
Isolating data with separate knowledge bases ensures that each specialized collaborator agent retrieves only the information relevant to its department. This improves response accuracy, reduces hallucinations, and supports privacy controls because clinical content, claims content, and scheduling content can have different access policies. IAM-based filtering ensures that each agent has permission only to the knowledge base it is authorized to use.
Routing patient inquiries through a supervisor agent supports high concurrency and extensibility. New departments or features can be added by introducing new collaborator agents and knowledge bases without redesigning the entire system. Because routing is handled centrally, changes in classification logic do not require updates across many independent supervisors.
Using RAG within each collaborator agent ensures that responses are grounded in department-approved information sources, which is critical in healthcare settings to reduce unsafe or incorrect guidance. This approach also improves performance because each retrieval scope is smaller and more relevant, supporting thousands of parallel interactions.
Option B introduces manual handoffs that do not scale. Option C relies on rule-based routing inside one general agent, which becomes brittle and difficult to govern as complexity grows. Option D mixes all departments into a single knowledge base and merges responses externally, increasing risk of incorrect domain answers and operational overhead.
Therefore, Option A best meets the scalability, correctness, and multi-agent onboarding requirements.
NEW QUESTION # 122
A company is building a multicloud generative AI (GenAI)-powered secret resolution application that uses Amazon Bedrock and Agent Squad. The application resolves secrets from multiple sources, including key stores and hardware security modules (HSMs). The application uses AWS Lambda functions to retrieve secrets from the sources. The application uses AWS AppConfig to implement dynamic feature gating. The application supports secret chaining and detects secret drift. The application handles short-lived and expiring secrets. The application also supports prompt flows for templated instructions. The application uses AWS Step Functions to orchestrate agents to resolve the secrets and to manage secret validation and drift detection.
The company finds multiple issues during application testing. The application does not refresh expired secrets in time for agents to use. The application sends alerts for secret drift, but agents still use stale data. Prompt flows within the application reuse outdated templates, which cause cascading failures. The company must resolve the performance issues.
Which solution will meet this requirement?
Answer: C
Explanation:
Option A is the correct solution because it directly addresses all identified failure modes while preserving the existing Step Functions-based orchestration architecture with minimal redesign.
Using Step Functions Map states enables parallel execution of secret resolution workflows, which improves refresh latency for short-lived and expiring secrets. This ensures that secrets are refreshed in time before downstream agents require them. Passing updated secret metadata through Lambda outputs guarantees that subsequent steps always consume the latest resolved values, preventing agents from using stale data even after drift alerts are generated.
Versioning prompt flows in AWS AppConfig is critical to resolving cascading failures caused by outdated templates. AppConfig natively supports version control, validation, staged rollout, and rollback of configuration artifacts. By gating prompt flows through AppConfig, the company can immediately roll back faulty templates and prevent agents from reusing outdated instructions.
This solution maintains clear separation of concerns: Step Functions handle orchestration and parallelism, Lambda handles secret retrieval and metadata propagation, and AppConfig governs prompt lifecycle management. No additional event pipelines or custom retry coordination layers are required.
Option B oversimplifies the architecture and does not address secret lifecycle or drift. Option C introduces event-driven ordering complexity without solving prompt versioning. Option D introduces unnecessary tooling and dynamic prompt generation risk.
Therefore, Option A best resolves performance, correctness, and stability issues while minimizing operational overhead.
NEW QUESTION # 123
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?
Answer: B
Explanation:
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.
NEW QUESTION # 124
A financial services company uses Amazon Bedrock to analyze customer data that is stored in an Amazon S3 bucket. The data includes personally identifiable information (PII). The company must mask PII from foundation model (FM) responses.
Which solution will meet this requirement with the LEAST operational effort?
Answer: A
Explanation:
Option A is correct because Amazon Bedrock Guardrails provides a native sensitive-information filter for personally identifiable information. The filter can detect supported PII types and use the MASK action so detected values in model requests or responses are replaced with placeholders such as the PII type. This directly satisfies the requirement to mask PII from foundation model responses with minimal operational effort. It avoids creating and maintaining an additional preprocessing pipeline. Option B can detect or redact PII with Amazon Comprehend, but it requires Lambda logic and data-processing workflows before Bedrock invocation. Options C and D require scanning, copying, and segregating objects across S3 buckets and still do not directly enforce response masking. For response-level protection in Bedrock, the managed Guardrails capability is the simplest and most purpose-built control. AWS Documentation
NEW QUESTION # 125
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?
Answer: C
Explanation:
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.
NEW QUESTION # 126
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