Amazon Web Services Updated AIP-C01 Exam Questions and Answers by skyler

Option B is correct because Amazon Bedrock Knowledge Bases supports structured document metadata that can be stored separately from the text and used as filters during retrieval. Case type, jurisdiction, and case outcome are ideal filterable metadata attributes, while author and version fields preserve provenance for returned documents. AWS documentation confirms that S3 knowledge-base data sources can use sidecar metadata files and that metadata remains available for filtering even when it is excluded from the embedding. This gives the application precise semantic retrieval plus structured constraints without custom extraction at query time. Option A lacks structured filtering. Option C adds unnecessary Comprehend processing. Option D embeds metadata inside document text, which does not provide reliable field-level filtering and adds unnecessary agent-side extraction complexity. AWS Documentation

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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Versioning prompt flows in AWS AppConfig is critical to resolving cascading failures caused by outdated templates.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prompt flows through AppConfig, the company can immediately roll back faulty templates and prevent agents from reusing outdated instructions.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.

Option B best meets all stated requirements by correctly combining PII protection , evaluation before launch , and data residency compliance using Amazon Bedrock Guardrails. Amazon Bedrock guardrails provide native sensitive information filtering that operates inline during model invocation, making them well suited for preventing personal data exposure in customer-facing AI assistants.

The requirement to evaluate how effective the AI assistant is at preventing exposure before release is best addressed by using mask mode during development and testing . Mask mode allows responses to be generated while automatically redacting detected personal information, making it easy for developers and reviewers to see where and how PII would have appeared. This provides concrete validation that the guardrail rules are correctly configured without fully blocking responses, which is ideal for quality assurance and pre-production evaluation.

For production , switching the guardrail to block mode ensures that responses containing personal information are fully prevented from being returned to users. This offers the strongest protection and aligns with compliance expectations for customer account data. Block mode is appropriate once confidence in the guardrail configuration has been established during testing.

The data residency requirement is addressed by deploying a copy of the guardrail in each AWS Region where the application operates. Amazon Bedrock guardrails are Region-specific resources, and using Region-local guardrails ensures that inference, filtering, and enforcement all occur within the same Region as the customer data. This avoids cross-Region processing and helps the company comply with regulatory and contractual data residency policies.

Option A and D incorrectly rely on cross-Region guardrails, which can violate data residency constraints. Option C focuses on topic filtering rather than sensitive information filtering and keeps detect mode enabled in production, which does not actively prevent PII exposure. Therefore, B is the only option that fully satisfies safety, compliance, and evaluation requirements.

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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