Authentic Amazon Web Services Certification Exam AIP-C01 Questions Answers

The correct combination is A, D, and F because these guardrail features directly map to the stated financial compliance and governance requirements.

Option A is required because denied topics guardrails are explicitly designed to block entire categories of requests, such as requests for guaranteed returns or specific stock recommendations. These are regulatory-sensitive scenarios where partial filtering is insufficient and full blocking is required to prevent violations.

Option D is correct because custom word filters are the appropriate guardrail mechanism to block references to specific competitor names. Content filters are category-based (such as hate, sexual, or violence-related content) and are not suitable for blocking organization-specific competitor references. Custom word filters allow precise blocking at both input and output stages.

Option F is required because a high grounding score threshold enforces that model outputs must be strongly supported by approved source material. This prevents the AI assistant from making speculative or unfounded claims that are not aligned with the company’s approved financial guidance, which is critical in regulated financial environments.

Option B is incorrect because content filters do not target domain-specific financial advice patterns. Option C is incorrect for the same reason—competitor names are not a content filter category. Option E would weaken factual grounding and increase hallucination risk.

Therefore, A, D, and F together provide topic blocking, competitor exclusion, and factual grounding enforcement.

The correct answers are A and D because they collectively satisfy the requirements for structured inputs, consistent output formatting, and non-blocking detection of bullying language.

Option A is required because Amazon Bedrock Prompt Management enables prompt templates with explicit input variables and defined output formats. By defining the four required inputs as variables, the company ensures that every invocation of the Amazon Nova Pro FM receives the correct structured inputs. Specifying the output format ensures consistent responses, which is essential for a grants application workflow. Adding the managed prompt to the prompts node of the flow allows Bedrock Flows to invoke the model using this standardized configuration.

Option D addresses the requirement to receive notifications when bullying language is detected without blocking responses. Amazon Bedrock guardrails support content filters with configurable actions. By applying the insults content filter and setting the response action to detect, the system flags responses containing bullying or insulting language while still allowing the response to be returned. This enables monitoring, alerting, and auditing without interrupting application functionality.

Option B is incorrect because setting the filter response to block contradicts the requirement not to block all flagged responses. Option C introduces a prompt router, which is unnecessary because the application uses a single Amazon Nova Pro FM. Option E incorrectly attempts to enforce input variables and output formatting through an inference profile, which does not provide prompt-level variable enforcement or formatting guarantees.

Therefore, A and D together provide structured prompt management and non-blocking safety detection with minimal operational complexity.

Option C is the best answer because it implements a decoupled, queue-based moderation pipeline that invokes the expensive model only when the low-latency model is not confident enough. Amazon SQS is designed to decouple distributed application components and support asynchronous processing. AWS documentation describes SQS as a fully managed message queuing service that enables decoupling and scaling of microservices, distributed systems, and serverless applications. This matches the requirement for high resiliency and sudden demand spikes because incoming requests can be buffered in a durable queue rather than overwhelming the model-processing layer.

Using AWS Fargate to process queue messages is also appropriate because Fargate provides serverless container compute for Amazon ECS or Amazon EKS workloads. It allows the company to run scalable processing workers without managing EC2 capacity directly. AWS Prescriptive Guidance includes architectures that use API Gateway, Amazon SQS, and AWS Fargate to process events asynchronously, which supports the same decoupled processing model required in this question.

The two-stage queue design also optimizes cost. The small, low-latency classifier is used first for all requests. Only requests with confidence below 0.65 are placed into the second queue and processed by the larger model. This avoids running the larger model for every moderation request. High-confidence results can be completed quickly by the first-stage processor, while uncertain results are isolated into an asynchronous second-stage workflow.

Option A is incorrect because it synchronously calls the larger model for low-confidence results, which violates the requirement to process low-confidence requests asynchronously. Option B is incorrect because it invokes both models for every request, increasing cost and eliminating the benefit of confidence-based escalation. Option D requires managing EC2 instances and uses keyword heuristics instead of model confidence, so it is less resilient and less aligned with Bedrock-based moderation. Therefore, option C is correct.