A company is using Amazon Bedrock to build a customer-facing AI assistant to handle sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross-Region failover capabilities.
Which solution will meet these requirements?
A. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.
B. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls for audits.
C. Deploy Amazon Comprehend custom classification to detect prompt injection attacks. Use Amazon API Gateway to validate requests. Use Amazon CloudWatch Logs with custom metrics to capture detailed intervention events.
D. Configure Amazon Bedrock guardrails to use custom content filters to protect against harmful content. Set the content filters to high. Use word filters to protect against known attack patterns. Configure cross-Region guardrail replication to provide failover capabilities. Store logs in AWS CloudTrail for compliance auditing.
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Guardrails’ prompt-attack content filter, set to high, detects prompt-injection attempts. A guardrail profile enables cross-Region inference for failover, and CloudWatch Logs can capture detailed guardrail intervention events for auditing. AWS WAF does not inspect prompt meaning, while CloudTrail records API activity rather than detailed safety interventions. Word filters alone are not a reliable defense against sophisticated attacks, and D describes cross-Region replication rather than the guardrail-profile approach.
Question 9
A university recently digitized a collection of archival documents, academic journals, and manuscripts. The university stores the digital files in an AWS Lake Formation data lake.
The university hires a GenAI developer to build a solution to allow users to search the digital files by using text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection based on text and metadata that is associated with the journal abstracts. The metadata of the digitized files does not contain keywords. The solution must match similar abstracts to one another based on the similarity of their text. The data lake contains fewer than 1 million files.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in the OpenSearch Neural Plugin for Amazon OpenSearch Service.
B. Use Amazon Comprehend to extract topics from the digitized files. Store the topics and file metadata in an Amazon Aurora PostgreSQL database. Query the abstract metadata against the data in the Aurora database.
C. Use Amazon SageMaker AI to deploy a sentence-transformer model. Use the model to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL database that has the pgvector extension.
D. Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL Serverless database that has the pgvector extension.
Show Answer
Correct Answer: D
Explanation: The requirement is semantic search over fewer than 1 million documents with minimal operational overhead. Amazon Titan Embeddings in Amazon Bedrock provides a fully managed embedding model, avoiding the need to deploy and manage ML infrastructure. Aurora PostgreSQL Serverless with the pgvector extension supports vector similarity search while minimizing database administration through serverless scaling. Amazon Comprehend topic extraction is not semantic vector search, SageMaker requires managing model endpoints, and OpenSearch Service introduces a dedicated search cluster, which is more operationally involved than Aurora Serverless for this scale.
Question 10
A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.
The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.
Which solution will meet these requirements?
A. Configure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.
B. Configure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment. Deploy a copy of the guardrail to each Region where the company operates.
C. Configure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.
D. Configure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.
Show Answer
Correct Answer: B
Explanation: Sensitive information filters are the appropriate Bedrock guardrail feature for preventing exposure of personal information. Using mask mode during development and testing allows evaluation of what would have been filtered while preserving visibility into model behavior. Switching to block mode in production provides stronger protection. To satisfy data residency requirements, the guardrail should be deployed separately in each AWS Region rather than using a cross-Region configuration.
Question 10
A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.
The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Choose three.)
A. Deploy Amazon Textract and Amazon Augmented AI (Amazon A2I) within the same Region to extract relevant data from the scanned documents. Route low-confidence pages to human reviewers.
B. Use AWS Lambda functions to detect and redact PII from submitted documents before inference. Apply Amazon Bedrock guardrails to prevent inappropriate or unauthorized content in model outputs. Configure Region-specific IAM roles to enforce data residency requirements and to control access to the extracted data.
C. Use Amazon Kendra and Amazon OpenSearch Service to extract field level values semantically from the uploaded documents before inference.
D. Store uploaded documents in Amazon S3 and apply object metadata. Configure IAM policies to store original documents within the same Region as each applicant. Enable object tagging for future audits.
E. Use AWS Glue Data Quality to validate the structured document data. Use AWS Step Functions to orchestrate a review workflow that includes a prompt engineering step that transforms validated data into optimized prompts before invoking Amazon Bedrock to assess loan applications.
F. Use Amazon SageMaker Clarify to generate fairness and bias reports based on model scoring decisions that Amazon Bedrock makes.
Show Answer
Correct Answer: A, B, D
Explanation: Amazon Textract extracts structured data, and Amazon A2I routes low-confidence results to human reviewers in the applicant’s Region. Lambda redacts PII before Bedrock inference, while Bedrock guardrails and IAM controls protect model use and data access. Region-local S3 storage and object tags address document residency and support audits. Glue Data Quality and prompt engineering are not required to meet these requirements.
Question 11
A company uses AWS Lake Formation to set up a data lake that contains databases and tables for multiple business units across multiple AWS Regions. The company wants to use a foundation model (FM) through Amazon Bedrock to perform fraud detection. The FM must ingest sensitive financial data from the data lake. The data includes some customer personally identifiable information (PM).
The company must design an access control solution that prevents PI I from appearing in a production environment. The FM must access only authorized data subsets that have PH redacted from specific data columns. The company must capture audit trails for all data access.
Which solution will meet these requirements?
A. Create a separate dataset in a separate Amazon S3 bucket for each business unit and Region combination. Configure S3 bucket policies to control access based on IAM roles that are assigned to FM training instances. Use S3 access logs to track data access.
B. Configure the FM to authenticate by using IAM roles and Lake Formation permissions based on LF-Tag expressions. Define business units and Regions as LF-Tags that are assigned to databases and tables. Use AWS CloudTrail to collect comprehensive audit trails of data access.
C. Use direct IAM principal grants on specific databases and tables in Lake Formation. Create a custom application layer that logs access requests and further filters sensitive columns before sending data to the FM.
D. Configure the FM to request temporary credentials from AWS STS. Access the data by using presigned S3 URLs that are generated by an API that applies business unit and Regional filters. Use AWS CloudTrail to collect comprehensive audit trails of data access.
Show Answer
Correct Answer: B
Explanation: Lake Formation is designed for fine-grained data lake access control, including LF-Tag-based authorization that scales across business units and Regions. Combined with Lake Formation permissions (including column-level controls and data filtering) it can ensure the foundation model only accesses authorized, redacted data subsets. CloudTrail integrates with Lake Formation to provide audit trails of data access. The other options rely primarily on S3 or custom application logic and do not provide Lake Formation's native fine-grained governance.
Question 11
A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type.
Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing.
The company needs a solution to fix these performance issues and enhance the user experience.
Which solution will meet these requirements?
A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
B. Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
C. Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
D. Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.
Show Answer
Correct Answer: A
Explanation: AWS Amplify AI Kit supports streaming responses through an AppSync-based application, allowing users to see generated content as it arrives instead of waiting for the full response from the synchronous Lambda resolver. This improves perceived latency and the experience for complex, longer-running questions. Increasing timeouts does not address the underlying wait, and InvokeModelWithResponseStream is not a direct replacement for the knowledge-base RetrieveAndGenerate operation.
Question 12
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain-specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon API Gateway to route incoming queries to an Amazon Bedrock agent. Configure the agent to use an Anthropic Claude model to decompose queries and an Amazon Titan model to expand queries. Create an Amazon Bedrock knowledge base to store the reference medical documents.
B. Configure an Amazon Bedrock knowledge base to store the reference medical documents. Enable query decomposition in the knowledge base. Configure an Amazon Bedrock flow that uses a foundation model (FM) and the knowledge base to support the application.
C. Use Amazon SageMaker Al to host custom ML models for both query decomposition and query expansion. Configure Amazon Bedrock knowledge bases to store the reference medical documents. Encrypt the documents in the knowledge base.
D. Create an Amazon Bedrock agent to orchestrate multiple AWS Lambda functions to decompose queries. Create an Amazon Bedrock knowledge base to store the reference medical documents. Use the agent's built-in knowledge base capabilities. Add deep research and reasoning capabilities to the agent to reduce ambiguity in the medical terminology.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock Knowledge Bases support managed retrieval-augmented generation, and enabling query decomposition helps answer complex multi-part queries with higher accuracy while reducing semantic dilution. Bedrock Flows provide a fully managed orchestration layer with minimal operational overhead compared with custom SageMaker models, Lambda orchestration, or multiple foundation models in an agent. This approach best satisfies the scalability and latency requirements using managed Bedrock capabilities.
Question 12
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.
Which solution will meet these requirements with the LEAST custom development effort?
A. Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.
B. Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
C. Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.
D. Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.
Show Answer
Correct Answer: C
Explanation: SageMaker Clarify is the option designed to evaluate bias across demographic groups. Publishing its fairness metrics to CloudWatch enables ongoing monitoring and threshold-based alerts; the metrics can also be used to compare separately evaluated prompt approaches in weekly reports. The other options rely on custom output analysis, misuse Bedrock Guardrails metrics as fairness measures, or use offline evaluation and intervention metrics that do not provide real-time demographic fairness monitoring.
Question 13
A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?
A. Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.
B. Use Amazon OpenSearch service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.
C. Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
D. Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.
Show Answer
Correct Answer: B
Explanation: A hybrid search that combines vector search (for semantic relationships) with keyword search (for exact legal terminology and citations) best fits legal research. Applying an Amazon Bedrock reranker further improves precision by reordering retrieved documents based on relevance, providing fast and accurate retrieval-augmented generation.
Question 13
A company is developing an internal generative AI (GenAI) assistant that uses Amazon Bedrock to summarize corporate documents for multiple business units. The GenAI assistant must generate responses in a consistent format that includes a document summary, classification of business risks, and terms that are flagged for review. The GenAI assistant must adapt the tone of responses for each user's business unit, such as legal, human resources, or finance. The GenAI assistant must block hate speech, inappropriate topics, and sensitive information such as personal health information.
The company needs a solution to centrally manage prompt variants across business units and teams. The company wants to minimize ongoing orchestration efforts and maintenance for post-processing logic. The company also wants to have the ability to adjust content moderation criteria for the GenAI assistant over time.
Which solution will meet these requirements with the LEAST maintenance overhead?
A. Use Amazon Bedrock Prompt Management to configure reusable templates and business unit-specific prompt variants. Apply Amazon Bedrock guardrails that have category filters and sensitive term lists to block prohibited content.
B. Use Amazon Bedrock Prompt Management to define base templates. Enforce business unit-specific tone by using system prompt variables. Configure Amazon Bedrock guardrails to apply audience-based threshold tuning. Manage the guardrails by using an internal administration API.
C. Use Amazon Bedrock with business unit-based instruction injection in API calls. Store response formatting rules in Amazon DynamoDB. Use AWS Step functions to validate responses. Use Amazon Comprehend to apply content filters after the GenAI assistant generates responses.
D. Use Amazon Bedrock with custom prompt templates that are stored in Amazon DynamoDB. Create one AWS Lambda function to select business unit-specific prompts. Create a second Lambda function to call Amazon Comprehend to filter prohibited content from responses.
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Prompt Management centralizes reusable prompt templates and business-unit-specific variants, allowing consistent response structure with tailored tone. Bedrock Guardrails provide configurable content filters and sensitive-information controls, which can be adjusted over time without custom post-processing or orchestration code.
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