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| Section | Weight | Objectives |
|---|---|---|
| Testing, Validation, and Troubleshooting | 11% | - Validate quality and reliability
|
| AI Safety, Security, and Governance | 20% | - Secure generative AI systems
|
| Implementation and Integration | 26% | - Implement generative AI features and capabilities
|
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Deploy and manage scalable solutions
|
| Foundation Model Integration, Data Management, and Compliance | 31% | - Manage data for generative AI applications
|
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NEW QUESTION # 123
An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs.
During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Option B is the best solution because it directly addresses the Step Functions 256 KB state payload quota by externalizing large intermediate artifacts to Amazon S3 and passing only lightweight references (URIs/keys) between states. This is a standard AWS pattern for workflows that produce large intermediate results, and it avoids introducing additional databases, compression logic, or cross-state-machine coordination that increases operational overhead.
In a multi-agent ReAct workflow, intermediate reasoning traces can be verbose and grow quickly as each agent produces chain-of-thought style artifacts, structured outputs, and supporting evidence. Step Functions is designed to orchestrate state transitions and pass JSON payloads, but large payloads should be stored outside the state machine and referenced by pointer values. Using Amazon S3 for intermediate outputs is operationally efficient because the application already uses S3 for storage, and S3 provides durable, low-cost storage with simple access patterns.
ResultPath and ResultSelector allow each state to store or reshape results so that only the required reference fields (such as s3Uri, object key, metadata, trace IDs) are forwarded to subsequent states. This preserves observability because the workflow can still log trace references, correlate steps with S3 objects, and store structured metadata for debugging. It also preserves the sequential validation design, keeping the existing ReAct pattern intact while preventing failures due to oversized payloads.
Option A adds additional services and read/write patterns that increase operational complexity. Option C introduces custom compression/decompression logic that is fragile, adds latency, and complicates troubleshooting. Option D increases orchestration overhead by splitting workflows and coordinating with events, which makes debugging harder and increases failure modes.
Therefore, Option B meets the payload limit requirement while keeping the architecture simple and observable.
NEW QUESTION # 124
A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.
The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.
Which solution will meet these requirements?
Answer: A
Explanation:
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.
NEW QUESTION # 125
A company wants to implement an Amazon Bedrock application that helps lawyers search thousands of legal documents. The solution must give lawyers the ability to query based on the type of legal case, jurisdiction, and case outcome. Queries must return information about document authors and versions. The solution must integrate with the company ' s existing tools and support future growth in volume and query complexity.
Which solution will meet these requirements?
Answer: D
Explanation:
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
NEW QUESTION # 126
A company is building a generative AI (GenAI) application that processes financial reports and provides summaries for analysts. The application must run two compute environments. In one environment, AWS Lambda functions must use the Python SDK to analyze reports on demand. In the second environment, Amazon EKS containers must use the JavaScript SDK to batch process multiple reports on a schedule. The application must maintain conversational context throughout multi-turn interactions, use the same foundation model (FM) across environments, and ensure consistent authentication.
Which solution will meet these requirements?
Answer: A
Explanation:
Option D is the correct solution because the Amazon Bedrock Converse API is purpose-built for multi-turn conversational interactions and is designed to work consistently across SDKs and compute environments. The Converse API standardizes how messages, roles, and context are represented, which ensures consistent behavior whether the application is running in AWS Lambda with Python or in Amazon EKS with JavaScript.
By passing previous messages in the messages array, the application explicitly maintains conversational context across turns without relying on external state stores. This approach is recommended by AWS for conversational GenAI workflows because it avoids state synchronization complexity and ensures deterministic model behavior across environments.
Using IAM roles for authentication provides a single, consistent security model for both Lambda and EKS.
IAM roles integrate natively with AWS SDKs, eliminating the need for custom authentication logic or environment-specific credentials. This aligns with AWS best practices for least privilege and simplifies governance.
Option A introduces inconsistent authentication and custom formatting logic, increasing complexity. Option B unnecessarily introduces ElastiCache for state management, which is not required when using the Converse API correctly. Option C stores state in process memory, which is unsafe and unreliable for serverless and containerized workloads.
Therefore, Option D best satisfies the requirements for conversational consistency, multi-environment support, shared model usage, and consistent authentication with minimal operational overhead.
NEW QUESTION # 127
A GenAI developer is using Amazon Bedrock to build an application. The application needs to interpret complex user requests in natural language, maintain semantic context across multiple interactions with the same user, and dynamically invoke more than 50 enterprise APIs based on reasoning rather than predefined workflows.
The application must autonomously coordinate backend tools and provide explainable results for compliance reviews. The GenAI developer wants to use Amazon Bedrock AgentCore to implement dynamic orchestration.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B is correct because the requirement is agentic, dynamic orchestration rather than a predefined workflow. AgentCore Memory can preserve conversational context, while AgentCore Gateway provides a unified MCP interface through which an agent can discover and invoke many tools and backend APIs. AWS documents support multiple Gateway targets and consolidated tool discovery, so adding or selecting enterprise capabilities does not require hard-coding every workflow path. AgentCore observability also exposes detailed traces covering processing steps, tool invocations, decision points, errors, and outputs, which supports compliance review and explainability. Option A is fundamentally static orchestration. Option C substitutes retrieval for API execution and still requires custom Lambda orchestration. Option D uses guardrails for a purpose they are not designed to perform; guardrails constrain content but are not a dynamic enterprise-tool orchestration engine. AWS Documentation
NEW QUESTION # 128
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