正确答案: A

Correct answer is A as we need to tackle 2 issues. First is to capture real time sensor data and store it for analysis. Second is to respond to emergency notifications events with low latency. First can be handled using Kinesis Firehose to load data in Redshift for analysis. Second can be handled using SQS for notifications and DynamoDB for quick analysis or processing.,Refer AWS documentation - Kinesis Firehose FAQs,Amazon Kinesis Data Firehose buffers incoming streaming data to a certain size or for a certain period of time before delivering it to destinations. You can configure buffer size and buffer interval while creating your delivery stream. Buffer size is in MBs and ranges from 1MB to 128MB for Amazon S3 destination and 1MB to 100MB for Amazon Elasticsearch Service destination. Buffer interval is in seconds and ranges from 60 seconds to 900 seconds. Please note that in circumstances where data delivery to destination is falling behind data writing to delivery stream, Firehose raises buffer size dynamically to catch up and make sure that all data is delivered to the destination.,Option B is wrong as SQS is not suitable for real time sensor data collection and not is DynamoDB for analytics. Also, Redshift with Kinesis would not provide quick handling for data as Kinesis works on buffer interval and buffer size.,Option C is wrong as DynamoDB is not ideal for batch analytics.,Option D is wrong as Kinesis would not work emergency services handling as it works on buffer interval and buffer size.