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| Section | Weight | Objectives |
|---|---|---|
| Foundation Model Integration, Data Management, and Compliance | 31% | - Data management and RAG architectures - Foundation model integration and usage - Compliance and responsible AI practices |
| Implementation and Integration | 26% | - Agent-based AI systems - Prompt engineering and prompt management - Embedding FMs into applications and workflows |
| AI Safety, Security, and Governance | 20% | - Guardrails and responsible AI implementation - Security controls for GenAI applications |
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Performance optimization - Cost optimization and token efficiency |
| Testing, Validation, and Troubleshooting | 11% | - Troubleshooting GenAI systems - Model evaluation and testing - Monitoring and observability |
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113. Frage
A financial services company processes more than 10,000 customer inquiries every day through a multi-agent GenAI application that uses Amazon Bedrock AgentCore. The application agents invoke several custom tools. During peak usage periods, users report that the custom tools experience up to 40% failure rates. The tools perform inconsistently for different teams at the company.
A GenAI developer must implement an observability solution that provides end-to-end visibility into agent interactions and tool behavior. The solution must use built-in Amazon Bedrock capabilities and must not require custom instrumentation. The GenAI developer needs a solution that requires minimal performance overhead.
Which solution will meet these requirements?
Antwort: B
Begründung:
Amazon Bedrock AgentCore Observability is the best fit because the requirement is specifically for built-in Amazon Bedrock visibility into agent interactions, tool behavior, and failures without custom instrumentation.
AWS documentation states that AgentCore provides built-in metrics, logs, and traces for AgentCore modular services, and that this data can be viewed in Amazon CloudWatch. AgentCore observability also supports sessions, traces, and spans, where traces represent full request-response cycles and spans represent operations inside a trace. This directly matches the need to analyze multi-agent interactions, tool invocations, latency, throttling, and error behavior during peak traffic.
Option A is also correct because Amazon Bedrock AgentCore integrates with AWS X-Ray for distributed tracing of AI agents and tools. AWS X-Ray support helps follow requests across service boundaries and identify performance bottlenecks in agent and tool execution. CloudWatch provides curated GenAI observability views for AgentCore and can show operational health, metrics, traces, sessions, and error breakdowns. This gives the developer end-to-end visibility with minimal additional overhead because the core telemetry is service-provided rather than extracted through a custom pipeline.
Option B is incorrect because Container Insights focuses on container infrastructure and would not provide native visibility into Bedrock AgentCore agent reasoning paths or tool-level traces. Polling APIs with Lambda adds custom logic and operational overhead. Option C is incorrect because a custom ETL pipeline, DynamoDB storage, and QuickSight reporting do not satisfy the "built-in Amazon Bedrock capabilities" and
"minimal overhead" requirements. Option D starts correctly with AgentCore Observability, but then adds custom Lambda processing and Grafana, which introduces unnecessary custom instrumentation and processing. Therefore, option A is the most AWS-aligned solution.
114. Frage
A company uses an application to process customer support tickets. The company wants to integrate AI- powered sentiment analysis and auto-response generation into the application by using Amazon Bedrock. The company wants to prioritize urgent issues and reduce initial response times by 40% compared to manual responses. The solution must process 100 concurrent webhook requests with response times under 500 ms.
The solution must maintain 99.9% availability across multiple AWS Regions and authenticate all incoming requests. The company must avoid any authentication failures. The company does not want to modify the existing application infrastructure, which includes several ticketing systems that use multiple webhook authentication methods. The solution must support scaling to handle occasional spikes up to 250,000 daily tickets during peak periods. Which solution will meet these requirements?
Antwort: C
Begründung:
To handle high concurrency (100+ requests) with sub-500 ms response times and diverse authentication methods without infrastructure changes, Amazon API Gateway with Lambda authorizers is the optimal choice. The Lambda authorizers can evaluate multiple authentication tokens or signatures centrally before the request reaches the processing logic, preventing unauthorized traffic and potential authentication failures at scale. AWS Lambda integrated with Amazon Bedrock provides the scalability to handle ticket surges (up to
250,000 daily) without over-provisioning resources. For high availability (99.9%) and multi-region resilience, storing the resulting sentiment and responses in Amazon DynamoDB global tables ensures that data is accessible across regions with minimal latency. Option B is less secure due to the " NONE " auth type, and Option C introduces queuing latency that may exceed the 500 ms target.
115. Frage
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity.
The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option C is the correct solution because it uses a single, well-tuned Amazon Bedrock guardrail that applies different actions to different content types, which is the recommended approach for minimizing false positives while enforcing strong policy controls.
Setting content filters to medium rather than high reduces overblocking of benign customer conversations while still preventing harmful content. Amazon Bedrock guardrails are designed to balance precision and recall, and medium sensitivity is commonly recommended for customer-facing financial services use cases.
Denied topics explicitly prevent the assistant from discussing investment advice, which is a regulatory requirement. Including definitions and sample phrases improves detection accuracy and reduces ambiguity.
Sensitive information filters support different actions per context. Masking PII in responses preserves conversational usefulness for legitimate customer support while preventing exposure of sensitive data.
Blocking sensitive financial information in inputs prevents downstream processing of disallowed content before it reaches the foundation model.
Critically, enabling both input and output evaluation ensures that guardrails are applied consistently at every stage of interaction. Custom blocked messages and audit logging provide clear compliance evidence for regulators and internal audits.
Option A causes excessive false positives by blocking all PII outright. Option B introduces unnecessary complexity and is not how Bedrock guardrails are intended to be applied. Option D uses orchestration logic that Bedrock guardrails already handle natively.
Therefore, Option C best satisfies enforcement, flexibility, auditability, and accuracy requirements.
116. Frage
A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model.
The system must respond in near real time for high-confidence results. The system must process low- confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system.
Which solution will meet these requirements?
Antwort: D
Begründung:
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.
117. Frage
A company has a customer service application that uses Amazon Bedrock to generate personalized responses to customer inquiries. The company needs to establish a quality assurance process to evaluate prompt effectiveness and model configurations across updates. The process must automatically compare outputs from multiple prompt templates, detect response quality issues, provide quantitative metrics, and allow human reviewers to give feedback on responses. The process must prevent configurations that do not meet a predefined quality threshold from being deployed.
Which solution will meet these requirements?
Antwort: D
Begründung:
Option B is the correct solution because Amazon Bedrock evaluation jobs are purpose-built to assess prompt effectiveness, model behavior, and response quality in a repeatable and automated manner. Evaluation jobs support both quantitative metrics and LLM-based judgment, making them suitable for detecting subtle response quality regressions that simple sentiment or latency metrics cannot capture.
By using custom prompt datasets, the company can consistently test multiple prompt templates and model configurations against the same inputs. This enables accurate comparison across updates and eliminates variability introduced by live traffic sampling. Amazon Bedrock evaluation jobs also support structured scoring outputs, which can be used to enforce objective quality thresholds.
Integrating evaluation jobs directly into AWS CodePipeline ensures that quality checks are automatically triggered whenever prompt templates or configurations change. This creates a gated deployment workflow in which only configurations that meet or exceed the predefined quality threshold are promoted. This directly satisfies the requirement to prevent low-quality configurations from being deployed.
Human reviewers can be incorporated by reviewing evaluation results and scores produced by the jobs, enabling informed feedback without manual data collection. Option A and D rely on custom frameworks and indirect quality signals, increasing complexity and reducing reliability. Option C focuses on operational health rather than response quality.
Therefore, Option B provides the most robust, scalable, and AWS-aligned quality assurance process for Amazon Bedrock-based applications.
118. Frage
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