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135. Frage
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option B is the best solution because hierarchical chunking is specifically designed to preserve broader semantic context while still enabling precise retrieval at paragraph or sub-paragraph granularity. The problem described-answers citing irrelevant sections when a query spans multiple paper sections-often occurs when chunks are either too small (losing cross-paragraph context) or too "flat" (retrieving isolated snippets without their surrounding rationale).
In a scientific paper, related information is frequently distributed across methodology, results, and discussion.
Flat, fixed-size chunking (Option A) can split these logically connected ideas into separate chunks, causing retrieval to surface fragments that match a term but not the full intent. Semantic chunking (Option C) improves boundary placement, but it does not inherently provide a multi-resolution structure that helps preserve section-level continuity at massive scale.
Hierarchical chunking solves this by creating parent chunks (larger context windows) that capture broader section context and child chunks (smaller units) that retain retrieval precision. When the retriever identifies relevant child chunks, it can also bring in the associated parent context so the foundation model sees the surrounding methodological or discussion framing. The defined overlaps further reduce the risk that key transitions or references are split across chunks.
This approach is well suited for a corpus of 25 million papers because it improves relevance without requiring a custom reranking model or a manual preprocessing pipeline. It remains operationally efficient because it is configured at the knowledge base level rather than implemented through custom code per document.
Option D introduces high operational complexity and inconsistent document handling at scale. Therefore, Option B best meets the requirement to preserve semantic context across related paragraphs and improve citation relevance across scientific paper sections.
136. Frage
A company is building a multicloud generative AI (GenAI)-powered secret resolution application that uses Amazon Bedrock and Agent Squad. The application resolves secrets from multiple sources, including key stores and hardware security modules (HSMs). The application uses AWS Lambda functions to retrieve secrets from the sources. The application uses AWS AppConfig to implement dynamic feature gating. The application supports secret chaining and detects secret drift. The application handles short-lived and expiring secrets. The application also supports prompt flows for templated instructions. The application uses AWS Step Functions to orchestrate agents to resolve the secrets and to manage secret validation and drift detection.
The company finds multiple issues during application testing. The application does not refresh expired secrets in time for agents to use. The application sends alerts for secret drift, but agents still use stale data. Prompt flows within the application reuse outdated templates, which cause cascading failures. The company must resolve the performance issues.
Which solution will meet this requirement?
Antwort: C
Begründung:
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.
137. 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.
138. Frage
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?
Antwort: A
Begründung:
When a foundation model fails to include specific required content or fails to integrate it coherently, prompt engineering techniques like output indicators or " wrappers " are highly effective. By explicitly defining where the promotional message should appear (e.g., " The response must end with the following message:
[PROMO TEXT] " ) or providing an example output structure, the developer reinforces the constraint within the model ' s generation path. This is more direct and less computationally expensive than generating multiple variants and reranking them (Option B) or adding complex post-processing layers (Option C). Guardrails (Option A) are intended for filtering harmful content rather than enforcing specific promotional copy insertion.
139. Frage
Example Corp provides a personalized video generation service that millions of enterprise customers use.
Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?
Antwort: B
Begründung:
Option A is the correct solution because Amazon Q Business is explicitly designed to provide secure, governed access to enterprise data while preserving customer ownership and control. Each customer maintains their own Amazon Q Business index, which ensures that data never leaves the customer's control boundary unless explicitly shared through approved access mechanisms.
By designating Example Corp as a data accessor, customers can allow controlled, auditable access to their indexed content through secure APIs. This model satisfies strict data governance requirements, including data ownership, access transparency, and revocation capability. Customers do not need to expose raw data or deploy infrastructure in Example Corp's environment.
Amazon Q Business provides high semantic accuracy through managed indexing, ranking, and retrieval optimizations. Because real-time access is not required, this approach avoids the complexity and latency challenges of live federated retrieval while still delivering fast query performance suitable for customer experience use cases.
Option B introduces unnecessary operational complexity by requiring real-time MCP servers per customer.
Option C requires customers to manage Amazon Bedrock knowledge bases and enable cross-account access, which increases integration complexity and governance risk. Option D requires shared Amazon Kendra indexes across accounts, which complicates access control and data ownership boundaries.
Therefore, Option A provides the cleanest, lowest-overhead architecture that meets data governance, accuracy, performance, and scalability requirements while minimizing operational burden for both Example Corp and its customers.
140. Frage
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