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A company needs a solution to ingest streaming sensor data from 100,000 devices, transform the...

A company needs a solution to ingest streaming sensor data from 100,000 devices, transform the data in near real time, and load the data into Amazon S3 for analysis. The solution must be fully managed, scalable, and maintain sub-second ingestion latency.

A.

Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache Flink to process the data in near real time. Use an Amazon Data Firehose stream to send processed data to Amazon S3.

B.

Use Amazon Simple Queue Service (Amazon SQS) standard queues to collect the sensor data. Invoke AWS Lambda functions to transform and process SQS messages in batches. Configure the Lambda functions to use an AWS SDK to write transformed data to Amazon S3.

C.

Deploy a fleet of Amazon EC2 instances that run Apache Kafka to ingest the data. Run Apache Spark on Amazon EMR clusters to process the data. Configure Spark to write processed data directly to Amazon S3.

D.

Implement Amazon EventBridge to capture all sensor data. Use AWS Batch to run containerized transformation jobs on a schedule. Configure AWS Batch jobs to process data in chunks. Save results to Amazon S3.

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