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NEW QUESTION # 43
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?
Answer: C
Explanation:
Option C is the correct solution because it enforces privacy controls at inference time, not at ingestion time, which is required when different user roles require different visibility into the same underlying data.
Using an S3 Lifecycle configuration ensures that documents older than 3 years are automatically removed, guaranteeing that the knowledge base references only compliant, recent medical reports. Scheduling Lambda- based syncs keeps the knowledge base aligned with the bucket contents without introducing complex per- upload orchestration.
The most important requirement is role-based PII exposure. Amazon Bedrock guardrails support dynamic application at inference time, allowing the system to select a guardrail configuration based on the authenticated user's Amazon Cognito group. Surgeons can receive full responses, while engineers receive responses with PII masked-without duplicating data or maintaining multiple knowledge bases.
This approach preserves a single source of truth for medical reports while enforcing privacy through response- level controls. It also maintains full auditability of access and redaction behavior.
Option A permanently removes PII and violates surgeon access requirements. Option B redacts data inconsistently and couples privacy logic to ingestion. Option D doubles storage, increases cost, and introduces data drift risk.
Therefore, Option C best meets privacy, compliance, scalability, and operational efficiency requirements.
NEW QUESTION # 44
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses.
The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes.
Which solution will meet these requirements?
Answer: A
Explanation:
Option B is correct because the workload has predictable short peak periods, strict low-latency requirements, and lower demand between spikes. Amazon SageMaker Serverless Inference is designed for intermittent or unpredictable traffic because SageMaker manages the infrastructure and the customer pays based on usage rather than continuously running idle instances. AWS documentation describes serverless inference as suitable when traffic is intermittent or unpredictable and when users do not want to manage instances or scaling policies.
Provisioned concurrency is the key part of this answer. For latency-sensitive serverless inference workloads, provisioned concurrency keeps the required number of serverless instances initialized and ready to respond.
AWS documentation states that SageMaker Serverless Inference integrates with Application Auto Scaling so provisioned concurrency can be scaled up or down based on a target metric or schedule. This fits a daily 2- hour peak window because the company can configure enough provisioned concurrency for 500 concurrent requests during peak periods and scale down when demand decreases.
Deploying each model to a separate serverless endpoint is also important. The categorization, extraction, and response-generation models likely have different memory, latency, and concurrency needs. Separate endpoints allow independent memory sizing, maximum concurrency configuration, monitoring, and scaling for each task. AWS API documentation also shows that serverless endpoint configuration includes maximum concurrency and provisioned concurrency settings, with provisioned concurrency required to be less than or equal to maximum concurrency.
Option A is less suitable because multi-model endpoints are usually best when many models can share infrastructure, but model loading and shared capacity can add latency risk. Option C assumes Amazon Bedrock provisioned throughput for models, but the company is developing specialized NLP models and needs task-specific deployment controls. Request batching can also add latency. Option D uses asynchronous inference, which is inappropriate for a less-than-500-ms synchronous response requirement. Therefore, option B best satisfies latency, concurrency, accuracy isolation, and resource optimization.
NEW QUESTION # 45
A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step- by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Option A is the best solution because it natively delivers retrieval grounding, source attribution, and low operational overhead through Amazon Bedrock Knowledge Bases. The key requirements are: retrieve from company data sources, cite sources, link claims to source documents, and keep latency under 3 seconds.
Knowledge Bases are a managed RAG capability that handles document ingestion, chunking, embeddings, retrieval, and assembly of context for model generation. This eliminates the need to build and maintain custom retrieval infrastructure.
Source attribution is crucial: the application must "link data claims to source documents." When source attribution is enabled, the RAG pipeline can return references to the underlying documents and segments used for generation. This enables traceable citations that can be surfaced to end users and used for internal auditing.
Using the Anthropic Claude Messages API (or equivalent conversational interface) with RAG allows the application to generate recommendations grounded in retrieved context while keeping responses conversational. Setting relevance thresholds helps reduce noisy retrieval, which supports both accuracy and latency targets by limiting the context passed to the model.
Storing reasoning and citations in Amazon S3 supports audit and retention needs with minimal operational burden. While the prompt may request step-by-step reasoning, AWS best practice is to produce user-facing explanations that are faithful and attributable without exposing internal reasoning traces unnecessarily. With source-grounded outputs, the system can provide concise rationale tied to citations while maintaining fast response times.
Option B emphasizes extended thinking, which increases latency and does not ensure source linkage. Option C adds significant operational overhead through custom model hosting and separate citation systems. Option D requires more custom tracking work than A while not improving retrieval attribution beyond what Knowledge Bases already provide.
Therefore, Option A best meets the requirements with the least operational overhead.
NEW QUESTION # 46
A financial technology company is using Amazon Bedrock to build an assessment system for the company's customer service AI assistant. The AI assistant must provide financial recommendations that are factually accurate, compliant with financial regulations, and conversationally appropriate. The company needs to combine automated quality evaluations at scale with targeted human reviews of critical interactions.
What solution will meet these requirements?
Answer: D
Explanation:
Option B meets the requirement to combine scalable automated evaluation with targeted human oversight using managed AWS GenAI capabilities. Amazon Bedrock evaluations enable systematic, repeatable quality assessment across large volumes of interactions. Using an LLM-as-a-judge approach with a strong evaluator model such as Anthropic Claude Sonnet allows the company to automatically score outputs for dimensions like factual accuracy, conversational appropriateness, and policy alignment. This directly supports "automated quality evaluations at scale" without building custom scoring models.
However, financial recommendations add higher risk because regulatory compliance requires additional enforcement beyond general quality scoring. Amazon Bedrock guardrails provide a dedicated policy enforcement layer that can block or intervene when responses violate compliance constraints. Guardrails are particularly important for preventing disallowed financial guidance patterns and ensuring consistent behavior across deployments.
The requirement also calls for "targeted human reviews of critical interactions." Amazon Augmented AI (A2I) is a managed human review service that supports routing specific items to human reviewers based on rules or confidence thresholds. In this design, the system can automatically send only high-risk or policy- flagged interactions to qualified financial experts for review, keeping human effort focused where it matters most while maintaining scale.
Option A is not scalable because it requires manual review of all responses. Option C relies on static rules and end-user feedback, which is insufficient for regulatory compliance and factual accuracy assurance. Option D provides monitoring but not structured quality evaluation or policy enforcement.
Therefore, Option B provides the most complete, AWS-aligned solution for scalable evaluation plus human oversight in a regulated financial context.
NEW QUESTION # 47
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?
Answer: D
Explanation:
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).
NEW QUESTION # 48
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