AWS Certified Professional Changed AIP-C01 Questions

Option B is the most appropriate solution because it directly aligns with AWS-recommended architectural patterns for building scalable, observable, and resilient generative AI applications on Amazon Bedrock. The requirements clearly distinguish between simple and complex routing decisions, and this option addresses both in an optimal way.

Simple routing based on file extension is latency sensitive. Handling this logic directly in the application code avoids unnecessary orchestration, state transitions, and service calls. This approach ensures that straightforward requests, such as routing images to vision-capable foundation models or text files to language models, are processed with minimal overhead and maximum performance.

For complex routing based on content semantics, AWS Step Functions is specifically designed for multi-step workflows that require analysis, branching logic, and error handling. Semantic routing often requires inspecting meaning, intent, or structure before selecting the appropriate foundation model. Step Functions enables this by orchestrating analysis steps and applying conditional logic to determine the correct model to invoke using the Amazon Bedrock InvokeModel API.

A key requirement is detailed execution history. Step Functions provides built-in execution tracing, including state inputs, outputs, and error details, which is essential for auditing, debugging, and compliance. Additionally, Step Functions supports native retry and catch mechanisms, allowing the workflow to automatically fall back to alternate foundation models if a primary model invocation fails. This directly satisfies the fallback requirement without introducing excessive custom code.

The other options lack one or more critical capabilities. Lambda-only logic lacks deep observability and structured fallback handling, SQS introduces additional latency and limited workflow visibility, and multiple coordinated workflows increase architectural complexity without added benefit.

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.

Options A, D, and E provide the required capabilities while minimizing custom infrastructure. Amazon Transcribe converts speech in uploaded media into text, Amazon Comprehend provides managed sentiment detection, and Bedrock Knowledge Bases can support embedding-based semantic retrieval over transcript content. DynamoDB is appropriate for lightweight, scalable counters such as views and positive-feedback totals and avoids operating a relational vector database solely for metrics. The Strands Agents SDK provides prebuilt HTTP-request tooling for invoking the existing chat API and supports tool-based and multi-agent orchestration patterns, reducing bespoke integration code. Option B adds Step Functions and Lambda when Strands can coordinate the required application interactions directly. Option C requires custom orchestration and error-management code. Option F introduces Aurora PostgreSQL and pgvector even though vector retrieval is already handled through the Bedrock knowledge-base layer. AWS Documentation

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Option B best meets the latency, resilience, and data residency requirements while keeping operational complexity low by using built-in Amazon Bedrock cross-Region inference behavior through inference profiles . Cross-Region inference profiles are designed to provide higher availability and better traffic absorption when a single Region experiences throttling, transient capacity constraints, or quota-related degradation. By selecting the appropriate geography-scoped inference profile (for example, a Europe-scoped profile for European users and a North America-scoped profile for North American users), the application can keep inference traffic within the required geographic boundary. This directly supports EU data residency needs because European requests can be served only by Europe-based Regions while still benefiting from multi-Region resilience inside Europe.

The question also highlights degradation when Regional traffic spikes hit quotas. Cross-Region inference profiles help mitigate these conditions by allowing Bedrock to serve requests from another Region within the same geography, improving continuity during spikes without requiring the company to implement custom retry-and-failover logic across Regions. This reduces development and operational burden compared to building and maintaining a bespoke routing and fallback system.

Using separate Amazon API Gateway HTTP APIs to direct European and North American users to the correct endpoints simplifies request routing and provides a clean boundary for compliance controls, logging, and monitoring. It also allows each geography to scale independently and maintain consistently low latency by keeping users close to the entry point and the Bedrock geography they must use.

Option A requires custom routing and manual operational monitoring and does not inherently solve quota-driven degradation. Option C adds significant complexity by embedding throttling retries and cross-Region selection logic in Lambda while still needing careful controls to prevent cross-border routing mistakes. Option D introduces the highest operational complexity and can inadvertently violate residency if failover crosses geographies unless additional safeguards are implemented.