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NEW QUESTION # 28
A healthcare company wants to develop a proof-of-concept application that uses Amazon Bedrock to automatically summarize medical documents. The company has 3 weeks to validate the application ' s accuracy. The application must comply with the company's data privacy policies. The application must include metrics to evaluate summarization accuracy and processing time. Which solution will meet these requirements?
Answer: D
Explanation:
For a 3-week proof-of-concept in a regulated field like healthcare, Retrieval Augmented Generation (RAG) is more efficient and safer than fine-tuning. RAG allows the use of anonymized patient records without risking the leak of sensitive data into the model ' s permanent memory. To evaluate accuracy quantitatively and rapidly, the " LLM-as-a-judge " pattern is recommended. Using a strong judge model to score the outputs of multiple candidate FMs provides objective metrics (e.g., factual alignment, completeness) that manual qualitative feedback (Option C) cannot scale to provide within the timeline. Fine-tuning (Option B) typically takes longer than 3 weeks to properly data-prep and validate for clinical accuracy.
NEW QUESTION # 29
A pharmaceutical company is developing a Retrieval Augmented Generation 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?
Answer: A
Explanation:
Option B is the best fit because hierarchical chunking is designed to preserve local detail while keeping broader document context available during retrieval, which directly addresses the problem of questions spanning methodology, results, and discussion. In large scientific papers, a single answer often depends on linked paragraphs across adjacent sections. If the knowledge base retrieves only small, isolated chunks, the RAG system can cite text that is semantically close to a query term but not contextually correct, producing inconsistent answers and irrelevant citations.
With hierarchical chunking, the knowledge base creates child chunks that are small enough for high- precision vector similarity matching, such as 200 tokens, which improves the likelihood that the retrieved text is tightly related to the user's query. At the same time, each child chunk is associated with a larger parent chunk, such as 1,000 tokens, which retains the surrounding narrative and section-level context. This structure helps the retrieval pipeline return passages that include the relevant subsection plus the explanatory framing that prevents misinterpretation, which is especially important in scientific writing where methods, results, and discussion are interdependent.
The configured overlap further reduces boundary effects where key statements split across chunks. This improves continuity for paragraphs that bridge sections, such as a results paragraph that references the methodological setup or a discussion paragraph interpreting a specific metric.
Option A can improve consistency slightly, but fixed-size chunking still risks separating related paragraphs and does not provide a built-in mechanism to retrieve broader context linked to precise matches. Option C can create more meaningful boundaries, but it does not guarantee the parent-level context that hierarchical chunking provides at retrieval time. Option D increases operational burden and is not practical at the scale of
25 million
NEW QUESTION # 30
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians.
The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?
Answer: D
Explanation:
AWS Step Functions provides native support for human-in-the-loop workflows, making it the best fit for regulatory oversight requirements. The waitForTaskToken integration pattern is explicitly designed to pause a workflow until an external actor-such as a human reviewer-completes a task.
In this architecture, AI-generated recommendations are sent to a human technician for review. The workflow pauses execution using a task token. Once the technician approves or rejects the recommendation, an AWS Lambda function calls SendTaskSuccess or SendTaskFailure, allowing the workflow to continue deterministically.
This approach ensures full auditability, as Step Functions records every state transition, timestamp, and execution path. Storing review outcomes in Amazon DynamoDB provides durable, queryable audit records required for regulatory compliance.
Option A requires custom orchestration and lacks native workflow state management. Option C incorrectly uses AWS Glue, which is not designed for approval workflows. Option D uses caching instead of durable audit storage and introduces unnecessary complexity.
Therefore, Option B is the AWS-recommended, lowest-risk, and most auditable solution for mandatory human review of AI outputs.
NEW QUESTION # 31
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgreSQL database.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?
Answer: C
Explanation:
Option D requires the least development effort because it uses a managed retrieval workflow that bundles the most time-consuming parts of semantic search: embedding generation, vector indexing, and natural language retrieval. With an Amazon Bedrock knowledge base, the application does not need to implement and operate separate services to (1) generate embeddings for hundreds of millions of records, (2) store and manage vectors, (3) build query-time embedding conversion logic, and (4) implement k-NN search orchestration.
Instead, the knowledge base is configured to automatically create embeddings during ingestion, and the application queries it using the Amazon Bedrock Retrieve API, which accepts natural language input and performs the vector search as a managed capability.
The performance requirement (95% of queries within 500 ms) is best served by a purpose-built vector search backend rather than running similarity search directly inside a transactional PostgreSQL system at this scale.
A knowledge base is designed for retrieval patterns and can be backed by scalable vector stores, which helps meet latency goals under heavy concurrency. The hourly freshness requirement maps naturally to ingestion updates: the pipeline can re-ingest updated restaurant details on a schedule so the knowledge base remains current without building custom re-embedding workflows in application code.
Cost-effective scaling during peak periods is also easier with a managed retrieval layer because scaling the retrieval workload is separated from the operational database. This avoids overprovisioning PostgreSQL for peak semantic-search traffic and reduces the engineering effort to tune performance, sharding, indexing, and retry logic.
Options B and C can work, but they require the team to build and maintain embedding pipelines, query embedding generation, vector index management, and operational scaling strategies. Option A does not provide semantic search because it relies on keyword-based matching rather than embeddings.
NEW QUESTION # 32
A healthcare company is deploying an AI system that uses a foundation model (FM) to help clinicians make diagnostic decisions. The company's ethics board requires the AI system to demonstrate fairness across patient demographic groups and comply with medical AI governance policies. During initial testing, the AI system provides recommendations without clear explanations or decision tracing. Clinicians are unable to review how the AI system produces diagnostic conclusions.
The company needs to implement a solution that provides transparent reasoning for AI outputs, enables systematic fairness testing, and ensures policy compliance for responsible AI use in healthcare settings. The solution must balance comprehensive explainability with real-time performance requirements. The solution must support rapid iteration for bias testing across multiple demographic variables. The solution must integrate seamlessly with existing clinical workflows while maintaining strict data privacy controls. The solution must handle complex medical and regulatory terminology.
Which solution will meet these requirements?
Answer: D
Explanation:
Option C is the best answer because the scenario is centered on a foundation-model GenAI application that needs explainability, fairness iteration, and responsible AI controls inside an Amazon Bedrock workflow.
Amazon Bedrock Agents support tracing, and AWS documentation states that traces can show the agent's path from user input to response, including action group inputs and outputs, knowledge base queries, and the reasoning the agent uses to decide which action or query to take. AWS also describes trace data as a way to understand how an agent arrived at a response. This directly addresses clinicians' need to review diagnostic reasoning and decision flow.
Amazon Bedrock Guardrails are also the right service for policy compliance in GenAI applications. AWS documentation states that Guardrails can implement safeguards aligned with responsible AI policies, configure denied topics, filter harmful content, and remove sensitive information. For healthcare and life sciences GenAI use cases, AWS Prescriptive Guidance recommends evaluating bias, fairness, and hallucinations and implementing guardrails to prevent harmful responses. This supports strict governance and privacy-sensitive clinical workflows.
The prompt testing and A/B testing component supports rapid iteration. AWS guidance for generative AI operations recommends prompt template management, creating and testing prompt variants, using A/B testing workflows for prompt variants, and analyzing performance against metrics. This is relevant for testing fairness behavior across demographic prompt sets and clinical scenarios.
Option A includes SageMaker Clarify, which is useful for bias detection and model explainability, but it is less directly aligned to Bedrock-native real-time tracing and policy enforcement for an FM application.
Option B handles medical text extraction and terminology but not reasoning transparency or governance.
Option D provides operational metrics and custom reports, but not native Bedrock reasoning traces or guardrails. Therefore, option C best meets the GenAI governance requirements.
NEW QUESTION # 33
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