AIP-C01 Exam Dumps - AWS Certified Generative AI Developer - Professional

Option C is the correct solution because it provides a secure, scalable, and standards-compliant way to expose an MCP server to an AI agent while enforcing strong user authorization. The Model Context Protocol supports HTTP-based transports for remote MCP servers, making Streamable HTTP the appropriate choice when the server is hosted as a managed service rather than a local process.

Hosting the MCP server in AWS Lambda enables automatic scaling and cost-efficient execution. By placing Amazon API Gateway in front of the Lambda function, the company creates a secure, managed HTTP endpoint that the AI agent can invoke reliably. This architecture cleanly separates transport, authentication, and business logic, which aligns with AWS serverless best practices.

Using Amazon Cognito to enforce OAuth 2.1 ensures that only authenticated and authorized users can access the MCP server. This satisfies security and compliance requirements when the MCP server handles sensitive user information. Cognito integrates natively with API Gateway, removing the need for custom authentication logic and reducing operational overhead.

Option A lacks user-level authorization controls. Option B and Option D rely on STDIO transport, which is intended for local or tightly coupled processes and is not suitable for distributed, serverless architectures. Option D also introduces security risks by handling credentials through environment variables.

Therefore, Option C best meets the requirements for secure access control, scalability, and correct MCP integration in an AWS-based AI agent architecture.

Option D is the correct solution because the Amazon Bedrock Converse API is purpose-built for multi-turn conversational interactions and is designed to work consistently across SDKs and compute environments. The Converse API standardizes how messages, roles, and context are represented, which ensures consistent behavior whether the application is running in AWS Lambda with Python or in Amazon EKS with JavaScript.

By passing previous messages in the messages array, the application explicitly maintains conversational context across turns without relying on external state stores. This approach is recommended by AWS for conversational GenAI workflows because it avoids state synchronization complexity and ensures deterministic model behavior across environments.

Using IAM roles for authentication provides a single, consistent security model for both Lambda and EKS. IAM roles integrate natively with AWS SDKs, eliminating the need for custom authentication logic or environment-specific credentials. This aligns with AWS best practices for least privilege and simplifies governance.

Option A introduces inconsistent authentication and custom formatting logic, increasing complexity. Option B unnecessarily introduces ElastiCache for state management, which is not required when using the Converse API correctly. Option C stores state in process memory, which is unsafe and unreliable for serverless and containerized workloads.

Therefore, Option D best satisfies the requirements for conversational consistency, multi-environment support, shared model usage, and consistent authentication with minimal operational overhead.

Option B is the best solution because it directly addresses the Step Functions 256 KB state payload quota by externalizing large intermediate artifacts to Amazon S3 and passing only lightweight references (URIs/keys) between states. This is a standard AWS pattern for workflows that produce large intermediate results, and it avoids introducing additional databases, compression logic, or cross-state-machine coordination that increases operational overhead.

In a multi-agent ReAct workflow, intermediate reasoning traces can be verbose and grow quickly as each agent produces chain-of-thought style artifacts, structured outputs, and supporting evidence. Step Functions is designed to orchestrate state transitions and pass JSON payloads, but large payloads should be stored outside the state machine and referenced by pointer values. Using Amazon S3 for intermediate outputs is operationally efficient because the application already uses S3 for storage, and S3 provides durable, low-cost storage with simple access patterns.

ResultPath and ResultSelector allow each state to store or reshape results so that only the required reference fields (such as s3Uri, object key, metadata, trace IDs) are forwarded to subsequent states. This preserves observability because the workflow can still log trace references, correlate steps with S3 objects, and store structured metadata for debugging. It also preserves the sequential validation design, keeping the existing ReAct pattern intact while preventing failures due to oversized payloads.

Option A adds additional services and read/write patterns that increase operational complexity. Option C introduces custom compression/decompression logic that is fragile, adds latency, and complicates troubleshooting. Option D increases orchestration overhead by splitting workflows and coordinating with events, which makes debugging harder and increases failure modes.

Therefore, Option B meets the payload limit requirement while keeping the architecture simple and observable.

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.

The least operational overhead approach is to use managed Amazon Bedrock model evaluation workflows with datasets stored in Amazon S3 , and then publish results into Amazon CloudWatch for dashboards. That is exactly what options B and C combine.

Step B correctly places standardized evaluation inputs in Amazon S3 and focuses on granting the evaluation workflow the right permissions to read those datasets. In practice, the key requirement is controlled access to the S3 objects used as evaluation datasets. Establishing IAM permissions and private access patterns (such as using VPC connectivity patterns where applicable to the organization’s networking posture) is aligned with enterprise requirements and avoids building custom storage or data distribution systems for evaluators.

Step C then operationalizes the evaluation lifecycle with minimal infrastructure: a scheduled AWS Lambda function starts evaluation jobs using the S3 dataset location, and a second Lambda function checks job status and pushes results and operational signals to CloudWatch . This meets the platform requirement to surface accuracy metrics in dashboards because CloudWatch metrics/logs can be visualized in dashboards and queried through CloudWatch Logs Insights . It also supports continuous, standardized comparisons across models without requiring developers to run ad-hoc experiments.

The alternatives introduce more operational burden. D and E rely on Amazon SageMaker -based tooling, notebook jobs, and open source evaluation frameworks, which require more environment management, dependency control, scaling considerations, and maintenance over time. A includes CORS, which is primarily a browser-access concern and does not address how Bedrock-managed evaluation jobs securely access S3 in the typical service-to-service pattern.

Therefore, B + C achieves standardized model evaluation, automated scheduling, and dashboard-ready observability with the smallest operations footprint.

Option B is the correct solution because Amazon Bedrock Knowledge Bases with the RetrieveAndGenerate API provide a fully managed Retrieval Augmented Generation (RAG) capability that directly addresses grounding, citation, and hallucination prevention with the least operational overhead.

Amazon Bedrock Knowledge Bases automatically manage document ingestion, chunking, embedding, retrieval, and ranking from approved data sources. When used with the RetrieveAndGenerate API, the model is constrained to generate responses only from retrieved, approved clinical documentation, significantly reducing the risk of hallucinations or unsupported claims. The API also returns explicit source citations, which satisfies regulatory and clinical transparency requirements without requiring custom comparison or validation logic.

This approach aligns with AWS best practices for healthcare GenAI workloads, where correctness and traceability are critical. Because retrieval and generation are tightly integrated, the system avoids multi-step orchestration, custom verification pipelines, or additional compute layers that would increase latency and maintenance burden.

Option A introduces Amazon Kendra and custom post-processing logic, increasing operational complexity. Option C focuses on entity extraction rather than controlled knowledge grounding and does not guarantee citation or hallucination prevention. Option D requires manual orchestration between retrieval and generation and custom verification logic, which increases development and maintenance effort.

Therefore, Option B delivers accurate, grounded, and cited clinical recommendations with minimal infrastructure and operational overhead.

Option A is the only listed choice that satisfies the hard requirement that PII must not reach the Bedrock model. AWS explicitly states that Knowledge Base guardrails apply to the user input and generated model response, but not to references retrieved from the Knowledge Base at runtime. Therefore, Options B, C, and D can still place PII-bearing retrieved chunks into model context even though a guardrail is configured. Amazon Comprehend can detect PII, enabling the Lambda preprocessing step in A to remove sensitive data before embeddings are created and before retrieved context is ever sent to the foundation model. Although semantic chunking would normally be preferable for records containing multiple unrelated topics, privacy is a mandatory constraint. Option A has more custom processing, but it is the only option that enforces PII removal upstream of model invocation.

Option B is the only solution that satisfies all strict real-time, streaming, and latency requirements. Amazon Transcribe streaming with partial results allows transcription fragments to be delivered before the speaker finishes a sentence. This significantly reduces perceived latency and enables downstream processing to begin immediately, which is essential for maintaining sub-1-second end-to-end response times.

Using Amazon Bedrock’s InvokeModelWithResponseStream API enables token-level or chunk-level streaming responses from the foundation model. This allows the GenAI assistant to begin delivering suggestions to call center agents incrementally instead of waiting for a full model response. This streaming inference capability is critical for interactive, real-time agent assistance use cases.

Amazon API Gateway WebSocket APIs provide fully managed, bidirectional communication between backend services and agent dashboards. This ensures that updates flow continuously to agents as new transcription fragments and model outputs become available, preserving real-time responsiveness without requiring custom socket infrastructure.

Option A introduces additional synchronous processing layers and storage writes that increase latency. Option C uses batch transcription and post-call processing, which cannot meet real-time requirements. Option D uses embeddings and asynchronous messaging, which are not suitable for live incremental suggestions and bidirectional streaming.

Therefore, Option B best aligns with AWS real-time GenAI architecture patterns by combining streaming transcription, streaming model inference, and managed bidirectional communication while maintaining low latency and operational simplicity.