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

Option B best meets the combined resiliency and observability requirements because it applies AWS-recommended retry behavior for transient throttling and enables true distributed tracing across service boundaries. During peak traffic, intermittent failures are commonly caused by throttling and other transient conditions. The AWS SDK standard retry mode provides exponential backoff with jitter, which reduces synchronized retry storms, prevents cascading failures, and improves overall system stability. Jitter is important because it spreads retry attempts over time, reducing load amplification during throttling events.

For observability, AWS X-Ray provides distributed tracing that follows a request across components such as API Gateway or load balancers, application services, and downstream calls to Amazon Bedrock. X-Ray can identify where latency is being introduced and which downstream call is contributing most to end-to-end response time. This is required to “identify latency contributors” and isolate performance issues under load.

The requirement also states that the company must correlate performance issues with specific FM characteristics. X-Ray annotations are designed for this purpose: the application can annotate traces with the model ID, inference parameters, region, or inference profile used. This enables filtering and analysis (for example, comparing latency or error patterns by model, parameter set, or endpoint configuration) without building a separate telemetry system.

Option A’s fixed-delay retries increase synchronized retry behavior and do not provide distributed tracing. Option C does not prevent cascading failures and cannot provide cross-service tracing. Option D is incorrect because CloudTrail is an audit logging service and does not provide distributed tracing for request latency analysis.

Therefore, Option B provides the correct combination of resilient retries and deep, model-correlated distributed observability for Amazon Bedrock workloads.

Option A is the correct solution because it uses native Amazon Bedrock governance and evaluation capabilities to meet regulatory, performance, and deployment timeline requirements with the least operational overhead.

Amazon Bedrock guardrails provide built-in safety controls that enforce responsible AI policies directly during inference. Custom content filters and toxicity detection protect customer interactions and prevent disallowed investment guidance patterns without requiring custom application logic. Guardrails operate inline and are optimized for low latency, which helps meet the strict 200 ms response-time requirement.

Hallucination detection is addressed through Amazon Bedrock Model Evaluation, which supports automated evaluation at scale using LLM-as-a-judge techniques. This enables the company to detect factual inaccuracies and policy violations systematically, without building custom evaluation pipelines or requiring extensive human review. Evaluation outputs can be surfaced as metrics.

Storing all prompt-response pairs in Amazon DynamoDB provides a low-latency, highly scalable audit store that aligns with financial regulatory requirements. Using TTL enforces data retention policies automatically, reducing compliance risk and storage overhead.

Amazon CloudWatch custom metrics integrate seamlessly with existing compliance dashboards, allowing near–real-time monitoring of safety interventions, hallucination rates, and drift indicators. CloudWatch anomaly detection can be applied to these metrics to surface behavior changes quickly.

Option B relies on custom Lambda logic and S3-based auditing, increasing latency and operational complexity. Option C introduces additional services that increase setup time and may exceed the 60-day deployment window. Option D uses non–Bedrock-native monitoring and adds unnecessary infrastructure layers.

Therefore, Option A provides the most complete, compliant, and low-overhead governance solution for a regulated GenAI financial services application.

Option C is the correct solution because Amazon Bedrock guardrails are purpose-built to enforce defense-in-depth safety controls for GenAI applications with minimal operational overhead. Guardrails provide managed, policy-based enforcement that operates before prompts are sent to the foundation model and after responses are generated, which directly satisfies the requirement that PII must not be sent to the model and must not appear in outputs.

By configuring a sensitive information policy, the application can automatically detect and redact PII in user inputs and model responses without building custom preprocessing pipelines. This approach is more reliable and scalable than regex or prompt engineering techniques, which are brittle and error-prone for sensitive data handling.

The topic policy capability in Amazon Bedrock guardrails allows the bank to explicitly block investment advice topics, ensuring regulatory compliance. This policy-based approach is safer and more auditable than attempting to steer the model only through prompt instructions.

Using the Converse API enables structured, standardized interactions with the model and supports consistent logging of requests and responses. Enabling delivery logging and image logging to Amazon S3 ensures that all customer interactions, including documents and images, are captured in a durable, auditable storage layer. This directly supports compliance, regulatory audits, and forensic analysis.

Option A incorrectly relies on Amazon Macie, which is designed for data-at-rest discovery rather than real-time conversational filtering. Option B introduces custom Lambda pipelines and topic modeling, increasing operational complexity. Option D relies on regex and prompt engineering, which do not meet financial-grade compliance standards.

Therefore, Option C delivers the strongest security, governance, and auditability with the least operational effort.