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Option B is the correct solution because it directly addresses both throughput bottlenecks and latency requirements using native Amazon Bedrock performance optimization features that are designed for real-time, high-volume generative AI workloads.

Amazon Bedrock supports cross-Region inference profiles, which allow applications to transparently route inference requests across multiple AWS Regions. During peak usage periods, traffic is automatically distributed to Regions with available capacity, reducing throttling, request queuing, and timeout risks. This approach aligns with AWS guidance for building highly available, low-latency GenAI applications that must scale elastically across geographic boundaries.

Token batching further improves efficiency by combining multiple inference requests into a single model invocation where applicable. AWS Generative AI documentation highlights batching as a key optimization technique to reduce per-request overhead, improve throughput, and better utilize model capacity. This is especially effective for lightweight, low-latency models such as Claude 3 Haiku, which are designed for fast responses and high request volumes.

Option A does not meet the requirement because purchasing provisioned throughput in a single Region creates a regional bottleneck and does not address multi-Region availability or traffic spikes beyond reserved capacity. Retries increase load and latency rather than resolving the root cause.

Option C improves application-layer scaling but does not solve model-side throughput limits. Client-side round-robin routing lacks awareness of real-time model capacity and can still send traffic to saturated Regions.

Option D is unsuitable because batch inference with asynchronous retrieval is designed for offline or non-interactive workloads. It cannot meet a strict 2-second response time requirement for an interactive AI assistant.

Therefore, Option B provides the most effective and AWS-aligned solution to achieve low latency, global scalability, and high throughput during peak usage periods.

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.

Option A best meets the requirements because it applies an AWS-aligned multi-agent pattern that cleanly separates responsibilities: a supervisor agent performs intent classification and orchestration, while specialized collaborator agents handle domain-specific tasks using the right knowledge sources. This structure is well suited for healthcare workflows where clinical questions, scheduling, and insurance processes require different policies, terminology, and data access boundaries.

The requirement for appropriate domain-specific responses is addressed by routing each user query to a department-focused collaborator agent that is grounded with its own department-specific knowledge base . Using Retrieval Augmented Generation with the correct knowledge base improves factual alignment and reduces cross-department leakage (for example, avoiding claims content in a clinical answer). It also supports better prompt grounding and more consistent tone and constraints per department.

The requirement to isolate data maps to using separate knowledge bases per agent and enforcing access through IAM controls , ensuring that each agent can retrieve only from the authorized datasets. This is important for minimizing unintended exposure of sensitive or irrelevant departmental data and supports governance and compliance needs.

For scalability and thousands of parallel interactions , this architecture minimizes contention and bottlenecks. Each collaborator agent can scale independently because requests are distributed across multiple agents and multiple retrieval backends. Operationally, onboarding new features is also simpler: the company can add a new collaborator agent (for example, “billing disputes” or “pharmacy refills”) with its own knowledge base and policies without redesigning the entire assistant.

Option B introduces unnecessary complexity with multiple supervisors and manual handoffs. Option C overloads a single agent with broad instructions and rule-based routing, which increases prompt complexity and reduces maintainability as features grow. Option D creates high operational complexity and risks inconsistent outputs when merging responses from parallel supervisors, and it weakens data isolation by using a shared knowledge base across agents.

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.