AWS Certified Generative AI – Professional (AIP-C01) — Question 55
A wildlife conservation agency operates zoos globally. The agency uses various sensors, trackers, and audiovisual recorders to monitor animal behavior. The agency wants to launch a generative AI (GenAI) assistant that can ingest multimodal data to study animal behavior.
The GenAI assistant must support natural language queries, avoid speculative behavioral interpretations, and maintain audit logs for ethical research audits.
Which solution will meet these requirements?
Answer options
- A. Ingest raw videos into Amazon Rekognition to detect animal postures and expressions. Use Amazon Data Firehose to stream sensor and GPS data into an Amazon S3 data lake. Prompt an Amazon Bedrock foundation model (FM) by using basic templates that are stored in AWS Systems Manager Parameter Store. Use IAM policies to control access. Use AWS CloudTrail for audit logging.
- B. Use Amazon SageMaker Processing and Amazon Transcribe to pre-process multimodal data. Ingest summaries into an Amazon Bedrock Retrieval Augmented Generation (RAG) knowledge base. Apply Amazon Bedrock guardrails to restrict speculative outputs. Use AWS AppConfig to manage prompt templates. Use AWS CloudTrail to log research activity for audits.
- C. Use Amazon OpenSearch Serverless to index behavioral logs and telemetry events. Use Amazon Comprehend to extract entities. Use Amazon Bedrock to build a layer to answer questions. Embed study summaries into OpenSearch Serverless documents. Use IAM to control access. Use AWS CloudTrail to log user interactions with the AI assistant.
- D. Configure Amazon Q Business to federate data across Amazon S3, Amazon Kinesis, and Amazon SageMaker Feature Store. Configure Amazon EventBridge to invoke data ingestion jobs. Use custom AWS Lambda functions to filter large language model (LLM) outputs for ethical compliance before returning results to users.
Correct answer: B
Explanation
Option B is correct because it employs Amazon SageMaker Processing and Amazon Transcribe to effectively preprocess multimodal data while ensuring that speculative outputs are restricted through guardrails. The other options either do not adequately preprocess the data or lack robust mechanisms to avoid speculative interpretations and maintain necessary audit trails.