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?
A. Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts to the company when usage approaches specified thresholds.
B. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when the application calls the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.
C. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the FM in the nearest secondary Region when the application reaches service quotas in the primary Region. Use intelligent routing to ensure compliance with data residency requirements.
D. Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in the application code to switch between Regions when throttling occurs. Use AWS Global Accelerator to route traffic to the appropriate endpoints based on user location.
Show Answer
Correct Answer: B
Explanation: Cross-Region inference profiles are the native Amazon Bedrock capability for distributing inference requests across multiple Regions while improving resilience during Regional capacity constraints. Using geographical profile IDs keeps European requests within European Regions to satisfy data residency requirements and similarly serves North American users locally for low latency. This minimizes operational complexity compared with building custom routing, retry, or failover logic.
Question 25
A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture.
The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries.
Which solution will meet these requirements?
A. Isolate data for each agent by using separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a supervisor agent to perform natural language intent classification on patient inquiries. Configure the supervisor agent to route queries to specialized collaborator agents to respond to department-specific queries. Configure each specialized collaborator agent to use Retrieval Augmented Generation (RAG) with the agent's department-specific knowledge base.
B. Create a separate supervisor agent for each department. Configure individual collaborator agents to perform natural language intent classification for each specialty domain within each department. Integrate each collaborator agent with department-specific knowledge bases only. Implement manual handoff processes between the supervisor agents.
C. Isolate data for each department in separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a single general-purpose agent. Configure multiple action groups within the general-purpose agent to perform specific department functions. Implement rule-based routing logic within the general-purpose agent instructions.
D. Implement multiple independent supervisor agents that run in parallel to respond to patient inquiries for each department. Configure multiple collaborator agents for each supervisor agent. Integrate all agents with the same knowledge base. Use external routing logic to merge responses from multiple supervisor agents.
Show Answer
Correct Answer: A
Explanation: A follows the recommended Amazon Bedrock multi-agent pattern: a single supervisor agent performs intent classification and routes requests to specialized collaborator agents. Each collaborator uses its own department-specific knowledge base with RAG, providing accurate domain-specific responses while keeping data isolated. This architecture is scalable, supports onboarding new departments/features, and can handle large numbers of parallel interactions more cleanly than multiple supervisors, a single general-purpose agent, or shared knowledge bases.
Question 27
An insurance company uses existing Amazon SageMaker AI infrastructure to support a web-based application that allows customers to predict what their insurance premiums will be. The company stores customer data that is used to train the SageMaker AI model in an Amazon S3 bucket. The dataset is growing rapidly. The company wants a solution to continuously re-train the model. The solution must automatically re-train and re-deploy the model to the application when an employee uploads a new customer data file to the S3 bucket.
Which solution will meet these requirements?
A. Use AWS Glue to run an ETL job on each uploaded file. Configure the ETL job to use the AWS SDK to invoke the Sage Maker AI model endpoint. Use real-time inference with the endpoint to re-deploy the model after it is re-trained on the updated customer dataset.
B. Create an AWS Lambda function and webhook handlers to generate an event when an employee uploads a new file. Configure SageMaker Pipelines to re-deploy the model after it is re-trained on the updated customer dataset. Use Amazon EventBridge to create an event bus. Set the Lambda function event as the source and SageMaker Pipelines as the target.
C. Create an AWS Step Functions Express workflow with AWS SDK integrations to retrieve the customer data from the S3 bucket when an employee uploads a new file to the S3 bucket. Use a SageMaker Data Wrangler flow to export the data from the S3 bucket to SageMaker Autopilot. Use SageMaker Autopilot to re-deploy the model after it has been re-trained on the updated customer dataset.
D. Create an AWS Step Functions Standard workflow. Configure the first state to call an AWS Lambda function to respond when an employee uploads a new file to the S3 bucket. Use a pipeline in SageMaker Pipelines to re-deploy the model after it has been re-trained on the updated customer dataset. Use the next state in the workflow to run the pipeline when the first state receives a response.
Show Answer
Correct Answer: D
Explanation: The required pattern is an event-driven ML workflow: an S3 upload triggers automation, an orchestrator coordinates the long-running retraining process, and SageMaker Pipelines performs retraining and model deployment. Option D uses Step Functions Standard to orchestrate the flow, invokes Lambda in response to the upload, and then starts a SageMaker Pipeline to retrain and redeploy the model. A incorrectly uses inference endpoints for retraining. B introduces unnecessary webhook/EventBridge flow and an incorrect event pattern. C relies on Data Wrangler and Autopilot, which are not required for an existing SageMaker training pipeline.
Question 28
A legal research company has a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock and Amazon OpenSearch Service. The application stores 768-dimensional vector embeddings for 15 million legal documents, including statutes, court rulings, and case summaries.
The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information.
Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds. The company expects storage needs for the application to grow from 90 GB to 360 GB within a year.
The company needs a solution to improve retrieval relevance and system performance at scale.
Which solution will meet these requirements?
A. Increase the embedding vector dimensionality from 768 to 4,096 without changing the existing chunking or pre-processing strategy.
B. Replace dynamic retrieval with static, pre-written summaries that are stored in Amazon S3. Use Amazon CloudFront to serve the summaries to reduce compute demand and improve predictability.
C. Update the chunking strategy to use semantic boundaries such as complete legal arguments, clauses, or sections rather than fixed token limits. Regenerate vector embeddings to align with the new chunk structure.
D. Migrate from OpenSearch Service to Amazon DynamoDB. Implement keyword-based indexes to enable faster lookups for legal concepts.
Show Answer
Correct Answer: C
Explanation: The primary issue is that fixed-size chunking splits semantically related legal content, reducing retrieval relevance. Using semantic chunking based on legal arguments, clauses, or sections preserves context, and regenerating embeddings ensures the vector index matches the improved chunk boundaries. This directly improves retrieval quality while avoiding the increased storage and latency costs of larger embeddings. The other options either increase cost without addressing the root cause (A), remove dynamic RAG capabilities (B), or replace semantic vector search with keyword lookup that is unsuitable for the stated requirements (D).
Question 29
A company is designing a solution that uses foundation models (FMs) to support multiple AI workloads. Some FMs must be invoked on demand and in real time. Other FMs require consistent high-throughput access for batch processing.
The solution must support hybrid deployment patterns and run workloads across cloud infrastructure and on-premises infrastructure to comply with data residency and compliance requirements.
Which combination of steps will meet these requirements? (Choose two.)
A. Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints.
B. Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high-volume workloads.
C. Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment.
D. Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges.
E. Use Amazon SageMaker JumpStart to host and invoke the FMs.
Show Answer
Correct Answer: B, C
Explanation: Provisioned Throughput in Amazon Bedrock provides dedicated capacity for consistent high-throughput foundation model inference, which fits batch processing requirements. For hybrid deployment spanning cloud and on-premises, SageMaker with Neo enables compiling and deploying supported models to edge/on-premises environments, and SageMaker endpoints plus Lambda orchestration can support inference workflows across environments. SageMaker asynchronous endpoints are not intended for low-latency real-time inference, and Bedrock does not offer customer-managed auto-scaling in the way described.
Question 30
Example Corp provides a personalized video generation service that millions of enterprise customers use. Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?
A. Ensure that each customer sets up an Amazon Q Business index that includes the customer's internal data. Ensure that each customer designates Example Corp as a data accessor to allow Example Corp to retrieve relevant content by using a secure API to enrich prompts at runtime.
B. Use federated search with Model Context Protocol (MCP) by deploying real-time MCP servers for each customer. Retrieve data in real time during prompt generation.
C. Ensure that each customer configures an Amazon Bedrock knowledge base. Allow cross-account querying so Example Corp can retrieve structured data for prompt augmentation.
D. Configure Amazon Kendra to crawl customer data sources. Share the resulting indexes across accounts so Example Corp can query each customer's Amazon Kendra index to retrieve augmentation data.
Show Answer
Correct Answer: A
Explanation: Amazon Q Business supports the data accessor pattern, allowing customers to retain ownership of their indexed data while granting a provider secure retrieval access for prompt enrichment. This fits the requirements for customer-controlled data, asynchronous indexing, high-quality semantic retrieval, low query latency, and minimal architectural complexity without deploying custom services in each customer environment. Amazon Bedrock Knowledge Bases do not provide the described cross-account querying pattern as the intended solution in this scenario, MCP requires per-customer deployment, and Kendra cross-account index sharing is not the recommended integration.
Question 32
A healthcare company is developing a document management system that stores medical research papers in an Amazon S3 bucket. The company needs to build a comprehensive metadata framework that will improve search precision for a generative AI (GenAI) application that analyzes the research papers. The metadata framework must include document timestamps, author information, and research domain classifications.
The solution must maintain a consistent metadata structure across all uploaded documents. The solution must give foundation models (FMs) the ability to understand document context without accessing the full content.
Which solution will meet these requirements?
A. Store document timestamps in Amazon S3 system metadata. Use S3 object tags to implement domain classification. Implement custom user-defined metadata to store author information.
B. Set up S3 Object Lock with legal holds to track document timestamps. Use S3 object tags to store author information. Implement S3 access points for domain classification.
C. Use S3 Inventory reports to track document timestamps. Create S3 access points to implement domain classification. Store author information in S3 Storage Lens dashboards.
D. Use custom user-defined metadata to store author information. Use S3 Object Lock retention periods to track document timestamps. Use S3 Event Notifications to implement domain classification.
Show Answer
Correct Answer: A
Explanation: Amazon S3 system metadata includes object timestamps such as Last-Modified. User-defined metadata is appropriate for descriptive attributes like author information, while S3 object tags are designed for categorical classifications such as research domains. Together these provide a consistent metadata structure that downstream AI systems can inspect without reading full document contents.
Question 33
A retail company is using Amazon Bedrock to develop a customer service AI assistant. Analysis shows that 70% of customer inquiries are simple product questions that a smaller model can effectively handle. However, 30% of inquiries are complex return policy questions that require advanced reasoning. The company wants to implement a cost-effective model selection framework to automatically route customer inquiries to appropriate models based on inquiry complexity. The framework must maintain high customer satisfaction and minimize response latency.
Which solution will meet these requirements with the LEAST implementation effort?
A. Create a multi-stage architecture that uses a small foundation model (FM) to classify the complexity of each inquiry. Route simple inquiries to a smaller, more cost-effective model. Route complex inquiries to a larger, more capable model. Use AWS Lambda functions to handle the routing logic.
B. Use Amazon Bedrock intelligent prompt routing to automatically analyze inquiries. Route simple product inquiries to smaller models, and route complex return policy inquiries to more capable larger models.
C. Implement a single-model solution that uses an Amazon Bedrock mid-sized foundation model (FM) with on-demand pricing. Include special instructions in model prompts to handle both simple and complex inquiries by using the same model.
D. Create separate Amazon Bedrock endpoints for simple and complex inquiries. Implement a rule-based routing system based on keyword detection. Use on-demand pricing for the smaller model and provisioned throughput for the larger model.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock Intelligent Prompt Routing is designed to automatically route requests to the most appropriate model based on the prompt, optimizing cost, latency, and quality with minimal implementation effort. It avoids building custom classification and routing logic while meeting the requirement to direct simple inquiries to smaller models and complex inquiries to more capable models.
Question 34
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?
A. 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.
B. Create an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database for each hotel. 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 for hotel staff.
C. Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide real-time room availability information. Schedule regular synchronization for less critical information.
D. Build a centralized Amazon Bedrock agent that uses multiple knowledge bases. Implement AWS IAM Identity Center with hotel-specific permission sets to control hotel staff data access.
Show Answer
Correct Answer: C
Explanation: Using a separate Amazon Bedrock knowledge base for each hotel in a multi-account architecture provides strong tenant isolation and independent access controls. It also scales with service quotas. Keeping rapidly changing room availability synchronized through direct ingestion while scheduling periodic synchronization for less frequently changing hotel information best matches the requirement for near real-time availability and consistent performance.
Question 35
A company wants to create an annual rewards program for its customers. The rewards that customers earn vary based on different parameters such as the categories of the items ordered and the customers' purchase history.
The company needs a generative AI (GenAI) solution that uses three Amazon Bedrock agents to help customers during online catalog browsing. The agents must use knowledge bases and action groups to handle the search, recommendation, and order modules. The modules must operate sequentially. An AWS Lambda function must calculate estimated rewards for each recommended item. The solution must provide graceful degradation during service disruptions.
Which solution will meet these requirements with the MOST operational efficiency?
A. Define an Amazon API Gateway REST API behind each agent. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Configure the second Lambda function with a retry/fallback mechanism.
B. Create an AWS Step Functions state machine with four tasks that run the agents and the rewards Lambda function. Set up retry and catch branches for each of the task steps.
C. Configure each agent with a separate retry/fallback mechanism. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Define an Amazon API Gateway REST API behind the second Lambda function.
D. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Create an AWS Step Functions state machine with one task that runs the second Lambda function. Set up retry and catch branches for the task step.
Show Answer
Correct Answer: B
Explanation: AWS Step Functions is the most operationally efficient choice for orchestrating sequential execution of three Amazon Bedrock agents and a Lambda function. It provides native workflow orchestration, state management, retries, and catch/fallback handling for each step without requiring custom orchestration code. This supports graceful degradation during service disruptions while minimizing operational overhead compared to Lambda-based orchestration.
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