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96. Frage
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?
Antwort: B
Begründung:
Option A best meets the combined requirements of low latency, stability, and validated safety controls by using purpose-built Amazon Bedrock features designed for production GenAI operations. The company's latency target of under 1 second and its observation of degradation during spikes strongly indicate capacity and throughput variability. Provisioned throughput for Amazon Bedrock is intended to deliver more predictable performance by reserving inference capacity for a chosen model, reducing throttling risk and stabilizing response times under load. This directly improves operational consistency across Regions where on-demand capacity can vary.
The requirement to "block unsafe or hallucinated recommendations" is most directly addressed by Amazon Bedrock Guardrails. Guardrails provide managed safety enforcement, including sensitive information controls and configurable content policies. Using semantic denial rules enables the application to prevent unsafe guidance such as dangerous brewing temperatures or other harmful procedural instructions, enforcing safety at the model boundary rather than relying on downstream filtering.
The remaining requirement is "99.5% output consistency for identical inputs." While generative models can be probabilistic, production systems achieve practical consistency by controlling prompt versions, inputs, and policy behavior. Amazon Bedrock Prompt Management supports controlled prompt lifecycle practices, including versioning and approval workflows, which reduce unintended drift across deployments and Regions. By ensuring the same approved prompt templates and parameters are used consistently, the company can materially improve repeatability for the same structured inputs and retrieval context, which is essential in multi-stage prompt chains.
The other options are incomplete. B improves experimentation and observability but does not enforce safety controls or stabilize latency. C can improve performance, but it does not provide validated safety enforcement at inference time. D can help retrieval relevance, but it does not address unsafe outputs or inference stability.
Therefore, A is the only option that simultaneously targets predictable latency, governance of prompt behavior, and strong safety controls within Amazon Bedrock.
97. Frage
A healthcare company is using Amazon Bedrock to build a system to help practitioners make clinical decisions. The system must provide treatment recommendations to physicians based only on approved medical documentation and must cite specific sources. The system must not hallucinate or produce factually incorrect information.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: B
Begründung:
Option B is the correct solution because Amazon Bedrock Knowledge Bases with the RetrieveAndGenerate API provide a fully managed Retrieval Augmented Generation (RAG) capability that directly addresses grounding, citation, and hallucination prevention with the least operational overhead.
Amazon Bedrock Knowledge Bases automatically manage document ingestion, chunking, embedding, retrieval, and ranking from approved data sources. When used with the RetrieveAndGenerate API, the model is constrained to generate responses only from retrieved, approved clinical documentation, significantly reducing the risk of hallucinations or unsupported claims. The API also returns explicit source citations, which satisfies regulatory and clinical transparency requirements without requiring custom comparison or validation logic.
This approach aligns with AWS best practices for healthcare GenAI workloads, where correctness and traceability are critical. Because retrieval and generation are tightly integrated, the system avoids multi-step orchestration, custom verification pipelines, or additional compute layers that would increase latency and maintenance burden.
Option A introduces Amazon Kendra and custom post-processing logic, increasing operational complexity.
Option C focuses on entity extraction rather than controlled knowledge grounding and does not guarantee citation or hallucination prevention. Option D requires manual orchestration between retrieval and generation and custom verification logic, which increases development and maintenance effort.
Therefore, Option B delivers accurate, grounded, and cited clinical recommendations with minimal infrastructure and operational overhead.
98. Frage
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) 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: D
Begründung:
Option B is the most appropriate solution because it directly uses Amazon Bedrock cross-Region inference profiles, which are designed to provide resilience and load distribution while respecting data residency boundaries. Cross-Region inference profiles allow applications to distribute inference requests across multiple Regions within a defined geographic boundary, such as Europe or North America, without requiring custom failover logic.
By specifying geographical codes in the inference profile ID, the application ensures that European user data is processed only within Europe-based Regions, satisfying regulatory requirements. At the same time, Bedrock automatically routes requests to healthy Regions within that geography when traffic spikes or service quotas are reached, improving availability and maintaining low latency.
Using separate Amazon API Gateway HTTP APIs for Europe and North America provides a clean, simple routing layer that directs users to the appropriate regional inference profile. This avoids complex custom routing or retry logic in application code and minimizes operational overhead.
Option A relies on custom routing and manual monitoring, which increases complexity and does not provide automatic resilience. Option C introduces custom retry and fallback logic that risks violating data residency requirements if misconfigured. Option D requires significant application-level failover logic and adds operational burden with Global Accelerator configuration.
Therefore, Option B best meets the requirements for low latency, data residency compliance, resilience during traffic spikes, and minimal operational complexity.
99. Frage
A healthcare company is using Amazon Bedrock to build a Retrieval Augmented Generation (RAG) application that helps practitioners make clinical decisions. The application must achieve high accuracy for patient information retrievals, identify hallucinations in generated content, and reduce human review costs.
Which solution will meet these requirements?
Antwort: D
Begründung:
Option D is the correct solution because it directly addresses all three requirements: high retrieval accuracy, hallucination detection, and reduced human review costs. AWS recommends a layered evaluation strategy for high-stakes domains such as healthcare, where generative outputs must be both accurate and safe.
Using an automated LLM-as-a-judge evaluation enables scalable, consistent assessment of generated responses for factual grounding, relevance, and hallucination risk. This automated screening significantly reduces the number of responses that require manual inspection. Only responses that fall below defined quality thresholds or exhibit ambiguous behavior are escalated to targeted human reviews, which optimizes review effort and cost.
The use of Amazon Bedrock built-in evaluations provides standardized metrics specifically designed for RAG systems, including retrieval precision, faithfulness to source documents, and hallucination rates. These evaluations integrate directly with Amazon Bedrock knowledge bases and models, eliminating the need to build and maintain custom evaluation pipelines.
Option A focuses on entity extraction confidence, which does not reliably detect hallucinations in generative text. Option B requires maintaining and scaling a separate fine-tuned evaluation model, increasing complexity and cost. Option C is useful for regression testing but cannot detect hallucinations in real-world, open-ended clinical queries.
Therefore, Option D provides the most effective and operationally efficient approach to maintaining clinical- grade accuracy while minimizing human review effort.
100. Frage
A company purchases Amazon Q Developer Pro subscriptions for 500 developers to improve code quality and productivity. The company needs to create an observability system that tracks adoption metrics across the company. The observability system must be able to identify active subscription users compared to underused subscriptions. The system must give the company the ability to recognize power users every quarter and to identify teams that require additional training. The system must provide visibility into usage patterns such as the number of lines of Amazon Q generated code that each user has accepted. Which solution will meet these requirements?
Antwort: D
Begründung:
Amazon Q Developer Pro provides a built-in administrator dashboard designed specifically for organizational observability. This dashboard provides native visibility into user-level metrics across the entire AWS Organization, allowing administrators to identify active vs. underused subscriptions and recognize power users. Crucially, it tracks high-level usage patterns, including code acceptance metrics (such as lines of code generated and accepted), which is a key requirement for measuring ROI and identifying training needs. Using the built-in dashboard provides the necessary insights with the least operational overhead, as it does not require building custom data pipelines (Option C) or complex log processing architectures (Option D).
101. Frage
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