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120. Frage
A company is using Amazon Bedrock to build a GenAI assistant that answers employee questions based on internal documentation. The company stores documents in Amazon S3, Atlassian Confluence, and an internal wiki system. The GenAI assistant must retrieve relevant content and provide grounded responses.
The solution must meet the following requirements:
* Integrate multiple document sources into a single retrieval layer.
* Support semantic search rather than keyword-only queries.
* Minimize custom ingestion and synchronization logic.
* Ensure that retrieved content can be directly used to augment the GenAI assistant ' s foundation model (FM).
Which solution will meet these requirements?
Antwort: D
Begründung:
Amazon Bedrock Knowledge Bases is designed specifically for managed retrieval-augmented generation. A knowledge base connects source repositories to an embedding model and vector store. During ingestion, source content is transformed into numerical vector embeddings. At retrieval time, the query is similarly represented so the knowledge base can compare semantic similarity and return passages that are conceptually relevant rather than depending exclusively on exact keyword matches.
Amazon Bedrock supports managed data-source connectors for multiple repositories. The AWS documentation lists Amazon S3, Confluence, Microsoft SharePoint, Salesforce, web crawling, and custom data sources among supported connection patterns. These connectors reduce the amount of ingestion, crawling, and synchronization code an organization needs to maintain.
For Atlassian Confluence specifically, Bedrock can crawl supported Confluence content and supports incremental synchronization for added, modified, or deleted material. Current managed Confluence integration also supports crawling pages, blog posts, and attachments, subject to the documented connector limitations.
This architecture supplies the retrieval layer needed to augment an FM with source-grounded context. It centralizes retrieval semantics while letting the application use Bedrock ' s managed ingestion and retrieval capabilities instead of implementing its own embedding pipelines.
B explicitly uses keyword-oriented mappings and therefore does not meet the semantic-search requirement. C could technically implement RAG, but Lambda-based embedding generation, synchronization, and custom retrieval logic create precisely the operational burden the company wants to avoid. D is not an appropriate semantic RAG architecture because ordinary DynamoDB queries do not automatically produce vector-based semantic retrieval from unstructured documentation.
For an internal wiki not covered by a built-in connector, the knowledge-base custom data-source capability can be used while keeping the overall retrieval interface centralized. Therefore, A provides the closest fit to every stated architectural requirement.
121. Frage
A company developed a multimodal content analysis application by using Amazon Bedrock. The application routes different content types (text, images, and code) to specialized foundation models (FMs).
The application needs to handle multiple types of routing decisions. Simple routing based on file extension must have minimal latency. Complex routing based on content semantics requires analysis before FM selection. The application must provide detailed history and support fallback options when primary FMs fail.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option B is the most appropriate solution because it directly aligns with AWS-recommended architectural patterns for building scalable, observable, and resilient generative AI applications on Amazon Bedrock. The requirements clearly distinguish between simple and complex routing decisions, and this option addresses both in an optimal way.
Simple routing based on file extension is latency sensitive. Handling this logic directly in the application code avoids unnecessary orchestration, state transitions, and service calls. This approach ensures that straightforward requests, such as routing images to vision-capable foundation models or text files to language models, are processed with minimal overhead and maximum performance.
For complex routing based on content semantics, AWS Step Functions is specifically designed for multi-step workflows that require analysis, branching logic, and error handling. Semantic routing often requires inspecting meaning, intent, or structure before selecting the appropriate foundation model. Step Functions enables this by orchestrating analysis steps and applying conditional logic to determine the correct model to invoke using the Amazon Bedrock InvokeModel API.
A key requirement is detailed execution history. Step Functions provides built-in execution tracing, including state inputs, outputs, and error details, which is essential for auditing, debugging, and compliance.
Additionally, Step Functions supports native retry and catch mechanisms, allowing the workflow to automatically fall back to alternate foundation models if a primary model invocation fails. This directly satisfies the fallback requirement without introducing excessive custom code.
The other options lack one or more critical capabilities. Lambda-only logic lacks deep observability and structured fallback handling, SQS introduces additional latency and limited workflow visibility, and multiple coordinated workflows increase architectural complexity without added benefit.
122. Frage
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option B best meets the latency, resilience, and data residency requirements while keeping operational complexity low by using built-in Amazon Bedrock cross-Region inference behavior through inference profiles. Cross-Region inference profiles are designed to provide higher availability and better traffic absorption when a single Region experiences throttling, transient capacity constraints, or quota-related degradation. By selecting the appropriate geography-scoped inference profile (for example, a Europe-scoped profile for European users and a North America-scoped profile for North American users), the application can keep inference traffic within the required geographic boundary. This directly supports EU data residency needs because European requests can be served only by Europe-based Regions while still benefiting from multi-Region resilience inside Europe.
The question also highlights degradation when Regional traffic spikes hit quotas. Cross-Region inference profiles help mitigate these conditions by allowing Bedrock to serve requests from another Region within the same geography, improving continuity during spikes without requiring the company to implement custom retry-and-failover logic across Regions. This reduces development and operational burden compared to building and maintaining a bespoke routing and fallback system.
Using separate Amazon API Gateway HTTP APIs to direct European and North American users to the correct endpoints simplifies request routing and provides a clean boundary for compliance controls, logging, and monitoring. It also allows each geography to scale independently and maintain consistently low latency by keeping users close to the entry point and the Bedrock geography they must use.
Option A requires custom routing and manual operational monitoring and does not inherently solve quota- driven degradation. Option C adds significant complexity by embedding throttling retries and cross-Region selection logic in Lambda while still needing careful controls to prevent cross-border routing mistakes. Option D introduces the highest operational complexity and can inadvertently violate residency if failover crosses geographies unless additional safeguards are implemented.
123. Frage
A company is developing a customer communication platform that uses an AI assistant powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes customer messages and generates initial response drafts.
The company wants to use Amazon Comprehend to implement layered content filtering. The layered content filtering must prevent sharing of offensive content, protect customer privacy, and detect potential inappropriate advice solicitation. Inappropriate advice solicitation includes requests for unethical practices, harmful activities, or manipulative behaviors.
The solution must maintain acceptable overall response times, so all pre-processing filters must finish before the content reaches the FM.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option D best satisfies all functional, performance, and governance requirements while minimizing architectural complexity. The requirement explicitly states that all filtering must complete before content reaches the foundation model, which rules out asynchronous or streaming-based approaches that could delay enforcement.
Amazon Comprehend supports toxicity detection, prompt safety classification, and PII detection with entity redaction as managed capabilities. Running these filters in parallel ensures low end-to-end latency, which is essential for customer-facing communication platforms. Parallel execution avoids the cumulative latency that would be introduced by sequential pipelines.
Toxicity detection identifies offensive or abusive content early. Prompt safety classification detects requests for unethical, harmful, or manipulative advice, which directly addresses inappropriate advice solicitation requirements. PII detection with entity redaction ensures that customer privacy is preserved before data is sent to the FM, preventing sensitive information from being processed or echoed in generated responses.
Configuring thresholds allows fine-grained control over sensitivity while maintaining acceptable false- positive rates. Using CloudWatch metrics and alarms enables continuous monitoring of filtering behavior and intervention rates without adding custom routing or human review pipelines that would slow responses.
Option A lacks PII redaction. Option B introduces unnecessary model-building complexity and delayed PII checks. Option C adds sequential latency and introduces human review routing, which violates the response- time requirement.
Therefore, Option D provides the most robust, performant, and AWS-aligned layered content filtering solution.
124. Frage
A healthcare company is developing an Amazon Bedrock-based AI clinical assistant. The company needs a solution to track model inputs and outputs so the company can perform offline quality evaluation and regression analysis. The solution must provide centralized, immutable audit records of all model inference and decision events. The company needs a solution that requires minimal runtime overhead so interactive latency remains stable during evaluation and auditing.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option C is correct because it combines complete inference capture, immutable retention, and offline evaluation without placing an evaluator in the synchronous request path. Amazon Bedrock model invocation logging can record request data, response data, and metadata to CloudWatch Logs and Amazon S3. S3 Object Lock provides write-once-read-many protection that can prevent retained audit objects from being overwritten or deleted. Bedrock model evaluation jobs can then assess stored or prepared response data offline, supporting regression and quality analysis without adding judge-model latency to every live clinical request. CloudTrail complements the audit record for service API activity. Option A depends on manual reviews. Option B records API activity but not the full inference payloads needed for quality evaluation, and ordinary versioning is not equivalent to WORM retention. Option D scores every request in production, adding runtime overhead.
AWS Documentation
125. Frage
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