Your company has developed and managed a Cash Logistics application. The application collects data from a variety of sources like Bank, Deposit Boxes, or ATMs. Then, it routes the physical cash to various points like Chain Stores, Banks, and ATM points to schedule the pickups and drop-offs. The application receives requests in different formats from different sources like CSV, XML, JSON, or even encoded data. Due to the nature of the application, all the transfer requests come before 24 hours of the actual time of scheduling so that they can be processed and prepared for the scheduling.

Answer options:

A.Use the AWS Glue to perform the extract and import the data to the source database for job scheduling. Trigger the AWS Batch jobs once the data is imported to perform the route schedule.
B.Use the AWS Batch to execute the processing jobs for different importers. Create separate job definitions and compute environments to support different import files based on customers.
C.Create different S3 buckets for a different source. Use the Lambda trigger to invoke after a new request has arrived. The Lambda will process the file and import the data into the target database for the job scheduling.
D.Use the EFS to import the data from on-premises data centers to the AWS. Run the EMR jobs which can pull the data from the EFS and import the data into the database for the job scheduling.
E.Import the files of data requests from the on-premises data center to S3. Store the files into one S3 bucket and allocate one folder for each customer.

Correct Answer: A, B, and E
Option A is CORRECT because the AWS Glue is a fully serverless ETL framework that can process a large amount of data set effectively and can handle the transformation with little to no coding requirements. It is possible to trigger the AWS Batch process once the AWS Glue import is completed. This can invoke the route scheduling jobs based on the imported data.
Option B is CORRECT because the AWS Batch can process a large number of jobs with varying compute requirements. The jobs can be prioritized as well as scheduled to perform after one another. The jobs run in the elastic containers and only consume the resources when there is anything to work on. This can be integrated with AWS Glue for the post-processing once the data is available for execution.
Option C is INCORRECT because running the data load and scheduling the jobs in the Lambda will not be possible as per the business requirements. Also, in the question, it mentions that it may take few minutes to an hour to process the routing information. The Lambda has a limited run time of 15 minutes per execution. So some jobs may not fit into that.
Option D is INCORRECT because different importers are required for the different file types, so there will be different EMR clusters that need to run and manage. EMR is technically the same as Glue, but it provides additional controls to manage the underlying Hadoop environment for the lower-level control. It may not be a suitable fit in the current context.
Option E is CORRECT because it can automatically sync the request files from the on-premise data center to the AWS environment. Once the files are available in the S3, other processes can be triggered via the S3 Event Triggers.