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Explanation:

Explanation

Option C is the correct answer because it best meets all the requirements:

Separate Access Controls: By implementing one Amazon Bedrock knowledge base for each hotel in a multi-account structure, the solution naturally enforces strict data isolation and access control at the hotel level without requiring complex policy logic or post-query filtering.
Near Real-time Room Availability: Using direct data ingestion provides near real-time updates for critical room availability information, which is essential for hotel operations.
Performance During Peak Usage: The multi-account structure with separate knowledge bases ensures consistent performance during peak usage periods, as each hotel's queries are isolated and don't compete for resources with other hotels.
Scalability: This approach aligns with AWS best practices for data isolation, access control, and scalable GenAI retrieval.
Flexible Synchronization: Scheduling regular synchronization for less critical information allows for efficient data management while maintaining real-time capabilities for critical data.

Why Other Options Are Incorrect:

Option A: A single knowledge base with combined data doesn't provide proper data isolation and would require complex filtering mechanisms to enforce hotel-specific access controls.
Option B: While EventBridge can handle updates, a centralized knowledge base still lacks proper data isolation and access control enforcement at the data level.
Option D: A centralized Agents solution with multiple knowledge bases doesn't inherently provide the same level of data isolation and access control as separate knowledge bases in a multi-account structure.

Explanation:

Explanation

Option C is the correct answer because it best meets all the requirements:

Separate Access Controls: By implementing one Amazon Bedrock knowledge base for each hotel in a multi-account structure, the solution naturally enforces strict data isolation and access control at the hotel level without requiring complex policy logic or post-query filtering.
Near Real-time Room Availability: Using direct data ingestion provides near real-time updates for critical room availability information, which is essential for hotel operations.
Performance During Peak Usage: The multi-account structure with separate knowledge bases ensures consistent performance during peak usage periods, as each hotel's queries are isolated and don't compete for resources with other hotels.
Scalability: This approach aligns with AWS best practices for data isolation, access control, and scalable GenAI retrieval.
Flexible Synchronization: Scheduling regular synchronization for less critical information allows for efficient data management while maintaining real-time capabilities for critical data.

Why Other Options Are Incorrect:

Option A: A single knowledge base with combined data doesn't provide proper data isolation and would require complex filtering mechanisms to enforce hotel-specific access controls.
Option B: While EventBridge can handle updates, a centralized knowledge base still lacks proper data isolation and access control enforcement at the data level.
Option D: A centralized Agents solution with multiple knowledge bases doesn't inherently provide the same level of data isolation and access control as separate knowledge bases in a multi-account structure.

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A hotel company wants to enhance a legacy Java-based property management system (PMS) by adding AI capabilities. The company wants to use Amazon Bedrock Knowledge Bases to provide staff with room availability information and hotel-specific details. The solution must maintain separate access controls for each hotel that the company manages. The solution must provide room availability information in near real time and must maintain consistent performance during peak usage periods.

Which solution will meet these requirements?

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DDucse

Last updated: March 24, 2026 at 07:15

Deploy a single Amazon Bedrock knowledge base that contains combined data for all hotels. Configure AWS Lambda functions to synchronize data from each hotel's PMS database through direct API connections. Implement AWS CloudTrail logging with hotel-specific filters to audit access logs for each hotel's data.

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Create an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database. Configure the rule to send updates to a centralized Amazon Bedrock knowledge base in a management AWS account. Configure resource-based policies to enforce hotel-specific access controls.