AIP-C01 Practice Exam Questions

A.  

Enable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.

B.  

Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded. Use a second Lambda function with Amazon Comprehend to redact PII for engineers. Use S3 Lifecycle rules to remove reports older than 3 years.

C.  

Set up an S3 Lifecycle configuration to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. When users interact with the AI assistant, apply a guardrail configuration selected based on the user’s Cognito user group to redact PII from responses when required.

D.  

Create a second knowledge base. Use Lambda and Amazon Comprehend to redact PII before syncing to the second knowledge base. Route users to the appropriate knowledge base based on Cognito group membership.

A.  

Use Amazon Comprehend to analyze and classify RAG responses and to extract medical entities and relationships. Use AWS Step Functions to orchestrate automated evaluations. Configure Amazon CloudWatch metrics to track entity recognition confidence scores. Configure CloudWatch to send an alert when accuracy falls below specified thresholds.

B.  

Implement automated large language model (LLM)-based evaluations that use a specialized model that is fine-tuned for medical content to assess all responses. Deploy AWS Lambda functions to parallelize evaluations. Publish results to Amazon CloudWatch metrics that track relevance and factual accuracy.

C.  

Configure Amazon CloudWatch Synthetics to generate test queries that have known answers on a regular schedule, and track model success rates. Set up dashboards that compare synthetic test results against expected outcomes.

D.  

Deploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use a built-in Amazon Bedrock evaluation to track retrieval precision and hallucination rates.

A.  

Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to orchestrate document file processing. Use Amazon Bedrock Knowledge Bases to process all multimedia. Store the content in Amazon DocumentDB with replication. Collaborate by using Amazon SNS topic subscriptions. Track changes by using Amazon Bedrock Agents.

B.  

Use Amazon Bedrock Data Automation (BDA) with foundation models (FMs) to process document files. Integrate BDA with Amazon Textract for PDF extraction and with Amazon Tran scribe for multimedia files. Store the processed content in Amazon S3 with versioning enabled. Store the metadata in Amazon DynamoD

B.  

B.  

C.  

Use Amazon Bedrock Data Automation (BDA) with Amazon SageMaker AI endpoints to host content extraction and summarization models. Use Amazon Bedrock Guardrails to extract content from all file types. Store document files in Amazon Neptune for time series analysis. Collaborate by using Amazon Bedrock Chat for real-time messaging.

D.  

Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to process batches of content files. Fine-tune foundation models (FMs) in Amazon Bedrock to classify documents across all content types. Store the processed data in Amazon ElastiCache (Redis OSS) by using Cluster Mode with sharding. Use Prompt management in Amazon Bedrock for version control.