Free Amazon Web Services AIP-C01 Practice Exam with Questions & Answers | Set: 4

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 B is the correct solution because legal research workloads require both semantic understanding and exact lexical precision, especially for statutes, citations, and domain-specific terminology. A hybrid search architecture directly addresses this need by combining vector similarity search with traditional keyword-based retrieval.

Vector search alone is often insufficient for legal research because exact phrases, citation formats, and jurisdiction-specific terms must be matched precisely. Keyword search ensures high recall and precision for citations and legal terms, while vector search captures deeper semantic relationships between legal concepts, precedents, and arguments. Amazon OpenSearch Service natively supports hybrid search, enabling efficient scoring and ranking without external orchestration.

Applying an Amazon Bedrock reranker model further improves relevance by reordering retrieved documents based on deeper contextual understanding. Reranking is especially valuable in legal research because multiple documents may appear relevant, but only a subset truly addresses the user’s legal question. The reranker optimizes final results before they are passed to the Anthropic Claude FM, improving answer accuracy and reducing hallucinations.

Option A relies on default vector search, which does not reliably handle citations and exact terminology. Option C focuses on query suggestions and post-processing rather than retrieval quality. Option D introduces unnecessary operational complexity by merging results across multiple systems.

Therefore, Option B best meets the requirements for precision, performance, and semantic understanding in a legal research AI assistant.