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NEW QUESTION # 110
A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?
Answer: B
Explanation:
Option A best meets the requirements because it directly implements a Retrieval Augmented Generation pattern for conversational recommendations using managed Amazon Bedrock capabilities and a scalable vector store. The company's source data already resides in Amazon S3, which aligns naturally with Amazon Bedrock Knowledge Bases ingestion workflows. A knowledge base can ingest book text, reviews, and metadata, generate embeddings using a supported embedding model, and persist those vectors in a purpose- built vector backend such as Amazon OpenSearch Service. This enables semantic retrieval that is well suited to conversation-driven intent, where user prompts are often descriptive and do not map cleanly to keyword filters.
The requirement to suggest books based on conversations implies the system must interpret natural language context and retrieve relevant passages, reviews, and metadata to ground the recommendation. Knowledge Bases provide managed orchestration for embedding creation and retrieval, which reduces development effort compared to building custom embedding pipelines. OpenSearch Service provides scalable vector search and k- nearest neighbors style similarity retrieval, which supports low-latency responses when properly indexed and sized.
For scaling to more than 10,000 concurrent users, the API layer design in option A is a common AWS pattern: Amazon API Gateway provides a managed front door with throttling and request handling, while AWS Lambda scales horizontally with demand and can invoke the knowledge base retrieval operations. This separates compute scaling from the vector store scaling and helps keep latency predictable under load.
Option B is not the best choice because DynamoDB is not the standard native vector store target for Amazon Bedrock Knowledge Bases in this context and would introduce additional implementation complexity around vector indexing and similarity search behavior. Option C requires substantial ML lifecycle work, model hosting, tuning, and continuous iteration to achieve quality recommendations at scale. Option D provides strong enterprise search, but it focuses on retrieval and FAQs rather than a managed RAG recommendation workflow grounded in embeddings and conversational context for generative responses.
NEW QUESTION # 111
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company ' s approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)
Answer: D,E,F
NEW QUESTION # 112
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?
Answer: A
Explanation:
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.
NEW QUESTION # 113
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: B
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 # 114
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 115
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