A company has a large number of spark jobs that need to run on an EMR cluster in AWS. They want some way to orchestrate the series of jobs. Which of the following can be used for this purpose?

Answer options:

A.AWS Step Functions
B.AWS SQS
C.Apache Hive
D.Apache Pig

Answer – A
An example of this is given in the AWS Documentation
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What if you have a simple use case, in which you want to run a few Spark jobs in a specific order, but you don’t want to spend time orchestrating those jobs or maintaining a separate application? You can do that today in a serverless fashion using AWS Step Functions. You can create the entire workflow in AWS Step Functions and interact with Spark on Amazon EMR through Apache Livy.
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Option B is invalid since this is queue-based service
Option C is invalid since this is an open-source, data warehouse, and analytic package that runs on top of a Hadoop cluster
Option D is invalid since this is an open-source Apache library that runs on top of Hadoop, providing a scripting language that you can use to transform large data sets without having to write complex code in a lower level computer language like Java.
For more information on this use case, please visit the url
https://aws.amazon.com/blogs/big-data/orchestrate-apache-spark-applications-using-aws-step-functions-and-apache-livy/