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A company is developing a highly available natural language processing (NLP) application.

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A company is developing a highly available natural language processing (NLP) application. The application handles large volumes of concurrent requests. The application performs NLP tasks such as entity recognition, sentiment analysis, and key phrase extraction on text data.

The company needs to store data that the application processes in a highly available and scalable database.

Options:

A.

Create an Amazon API Gateway REST API endpoint to handle incoming requests. Configure the REST API to invoke an AWS Lambda function for each request. Configure the Lambda function to call Amazon Comprehend to perform NLP tasks on the text data. Store the processed data in Amazon DynamoDB.

B.

Create an Amazon API Gateway HTTP API endpoint to handle incoming requests. Configure the HTTP API to invoke an AWS Lambda function for each request. Configure the Lambda function to call Amazon Translate to perform NLP tasks on the text data. Store the processed data in Amazon ElastiCache.

C.

Create an Amazon SQS queue to buffer incoming requests. Deploy the NLP application on Amazon EC2 instances in an Auto Scaling group. Use Amazon Comprehend to perform NLP tasks. Store the processed data in an Amazon RDS database.

D.

Create an Amazon API Gateway WebSocket API endpoint to handle incoming requests. Configure the WebSocket API to invoke an AWS Lambda function for each request. Configure the Lambda function to call Amazon Textract to perform NLP tasks on the text data. Store the processed data in Amazon ElastiCache.

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