Explanation:
Option B best fulfills all functional, scalability, and collaboration requirements by combining purpose-built AWS services with Amazon Bedrock capabilities. Amazon Bedrock Data Automation (BDA) is designed to orchestrate large-scale, multimodal data processing pipelines and integrates naturally with foundation models for summarization and concept extraction. Using BDA to process document files ensures consistent preprocessing and model invocation at scale, which is essential for handling more than 10,000 sources per day with high concurrency. \n\nIntegrating Amazon Textract for PDFs enables accurate extraction of structured and unstructured text from scanned and digital documents, while Amazon Transcribe is the appropriate service for converting recorded videos into text for downstream semantic analysis. Storing processed content in Amazon S3 with versioning enabled directly addresses the requirement for version control. Metadata storage in Amazon DynamoDB supports high-throughput, low-latency access patterns. \n\nReal-time collaboration is achieved through AWS AppSync GraphQL subscriptions combined with DynamoDB. AppSync enables real-time updates to connected clients whenever study materials are created or modified, making it well-suited for collaborative editing and live synchronization. \n\nThe other options misuse services: Amazon SNS does not support collaborative state synchronization, Amazon DocumentDB is not optimized for versioned document storage, Amazon Neptune is unsuitable for document-centric workloads, and Amazon ElastiCache is not designed for durable storage or version control.
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A company needs a system to automatically generate study materials from multiple content sources. The content sources include document files (PDF files, PowerPoint presentations, and Word documents) and multimedia files (recorded videos). The system must process more than 10,000 content sources daily with peak loads of 500 concurrent uploads. The system must also extract key concepts from document files and multimedia files and create contextually accurate summaries. The generated study materials must support real-time collaboration with version control.\n\nWhich solution will meet these requirements?
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 Transcribe for multimedia files. Store the processed content in Amazon S3 with versioning enabled. Store the metadata in Amazon DynamoDB. Collaborate in real time by using AWS AppSync GraphQL subscriptions and DynamoDB.
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.