A gas company needs to monitor gas pressure in their pipelines. Pressure data is streamed from sensors placed throughout the pipelines to monitor the data in real time. When an anomaly is detected, the system must send a notification to open valve. An Amazon Kinesis stream collects the data from the sensors and an anomaly Kinesis stream triggers an AWS Lambda function to open the appropriate valve.
Which solution is the MOST cost-effective for responding to anomalies in real time?

A. Attach a Kinesis Firehose to the stream and persist the sensor data in an Amazon S3 bucket. Schedule an AWS Lambda function to run a query in Amazon Athena against the data in Amazon S3 to identify anomalies. When a change is detected, the Lambda function sends a message to the anomaly stream to open the valve.

B. Launch an Amazon EMR cluster that uses Spark Streaming to connect to the Kinesis stream and Spark machine learning to detect anomalies. When a change is detected, the Spark application sends a message to the anomaly stream to open the valve.

C. Launch a fleet of Amazon EC2 instances with a Kinesis Client Library application that consumes the stream and aggregates sensor data over time to identify anomalies. When an anomaly is detected, the application sends a message to the anomaly stream to open the valve.

D. Create a Kinesis Analytics application by using the RANDOM_CUT_FOREST function to detect an anomaly. When the anomaly score that is returned from the function is outside of an acceptable range, a message is sent to the anomaly stream to open the valve.








 

Suggested Answer: A






This question is in BDS-C00 AWS Certified Big Data – Specialty Exam
For getting AWS Certified Big Data – Specialty Certificate



Disclaimers:
The website is not related to, affiliated with, endorsed or authorized by Amazon.
Trademarks, certification & product names are used for reference only and belong to Amazon.
The website does not contain actual questions and answers from Amazon's Certification Exam.