AIP-C01 Bedrock Practice Questions

An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations. The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company's product catalog. The company must resolve these issues. Which solution will meet this requirement?

• AA. Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning check. Set up provisioned throughput.
• BB. Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.
• CC. Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized.
• DD. Store product catalog data in Amazon OpenSearch Service. Validate the model's product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.

A company has a generative AI (GenAI) application that uses Amazon Bedrock to provide real-time responses to customer queries. The company has noticed intermittent failures with API calls to foundation models (FMs) during peak traffic periods. The company needs a solution to handle transient errors and provide detailed observability into FM performance. The solution must prevent cascading failures during throttling events and provide distributed tracing across service boundaries to identify latency contributors. The solution must also enable correlation of performance issues with specific FM characteristics. Which solution will meet these requirements?

• AA. Implement a custom retry mechanism with a fixed delay of 1 second between retries. Configure Amazon CloudWatch alarms to monitor the application's error rates and latency metrics.
• BB. Configure the AWS SDK with standard retry mode and exponential backoff with jitter. Use AWS X-Ray tracing with annotations to identify and filter service components.
• CC. Implement client-side caching of all FM responses. Add custom logging statements in the application code to record API call durations.
• DD. Configure the AWS SDK with adaptive retry mode. Use AWS CloudTrail distributed tracing to monitor throttling events.

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?

• AA. 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 routing logic.
• BB. 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.
• CC. 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.
• DD. 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.

A financial services company is deploying a generative AI (GenAI) application that uses Amazon Bedrock to assist customer service representatives to provide personalized investment advice to customers. The company must implement a comprehensive governance solution that follows responsible AI practices and meets regulatory requirements. The solution must detect and prevent hallucinations in recommendations. The solution must have safety controls for customer interactions. The solution must also monitor model behavior drift in real time and maintain audit trails of all prompt-response pairs for regulatory review. The company must deploy the solution within 60 days. The solution must integrate with the company's existing compliance dashboard and respond to customers within 200 ms. Which solution will meet these requirements with the LEAST operational overhead?

• AA. Configure Amazon Bedrock guardrails to apply custom content filters and toxicity detection. Use Amazon Bedrock Model Evaluation to detect hallucinations. Store prompt-response pairs in Amazon DynamoDB to capture audit trails and set a TTL. Integrate Amazon CloudWatch custom metrics with the existing compliance dashboard.
• BB. Deploy Amazon Bedrock and use AWS PrivateLink to access the application securely. Use AWS Lambda functions to implement custom prompt validation. Store prompt-response pairs in an Amazon S3 bucket and configure S3 Lifecycle policies. Create custom Amazon CloudWatch dashboards to monitor model performance metrics.
• CC. Use Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to ground responses. Use Amazon Bedrock Guardrails to enforce content safety. Use Amazon OpenSearch Service to store and index prompt-response pairs. Integrate OpenSearch Service with Amazon QuickSight to create compliance reports and to detect model behavior drift.
• DD. Use Amazon SageMaker Model Monitor to detect model behavior drift. Use AWS WAF to filter content. Store customer interactions in an encrypted Amazon RDS database. Use Amazon API Gateway to create custom HTTP APIs to integrate with the compliance dashboard.

A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real-time personally identifiable information (PII) filtering and audit trails to automatically report compliance. Which solution will meet these requirements?

• AA. Use AWS Glue Data Catalog to register all data sources and track lineage. Use Amazon Bedrock Guardrails PII filters. Enable AWS CloudTrail logging for all Amazon Bedrock API calls with Amazon S3 integration. Use Amazon Macie to scan stored data for sensitive information and publish findings to Amazon CloudWatch Logs. Create CloudWatch dashboards to visualize the findings and generate automated compliance reports.
• BB. Use AWS Config to track data source configurations and changes. Use AWS WAF with custom rules to filter PII at the application layer before Amazon Bedrock processes the data. Configure Amazon EventBridge to capture and route audit events to Amazon S3. Use Amazon Comprehend Medical with scheduled AWS Lambda functions to analyze stored outputs for compliance violations.
• CC. Use AWS DataSync to replicate data sources to track lineage. Configure Amazon Macie to scan Amazon Bedrock outputs for sensitive information. Use AWS Systems Manager Session Manager to log user interactions. Deploy Amazon Textract with AWS Step Functions workflows to identify and redact PII from generated reports.
• DD. Configure Amazon Athena to query data sources to analyze and report on data lineage. Use Amazon CloudWatch custom metrics to monitor PII exposure in Amazon Bedrock responses and establish AWS X-Ray tracing to generate an audit trail. Use an Amazon Rekognition Custom Labels model to detect sensitive information in the data that Amazon Bedrock processes.