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An organization is designing a mule application to support an all or nothing transaction between serval database operations and some other connectors so that they all roll back if there is a problem with any of the connectors
Besides the database connector , what other connector can be used in the transaction.
See the explanation below.
Correct answer is VM VM support Transactional Type. When an exception occur, The transaction rolls back to its original state for reprocessing. This feature is not supported by other connectors.
Here is additional information about Transaction management:
A mule application uses an HTTP request operation to involve an external API.
The external API follows the HTTP specification for proper status code usage.
What is possible cause when a 3xx status code is returned to the HTTP Request operation from the external API?
See the explanation below.
3xx HTTP status codes indicate a redirection that the user agent (a web browser or a crawler) needs to take further action when trying to access a particular resource.
An organization is migrating all its Mule applications to Runtime Fabric (RTF). None of the Mule applications use Mule domain projects.
Currently, all the Mule applications have been manually deployed to a server group among several customer hosted Mule runtimes.
Port conflicts between these Mule application deployments are currently managed by the DevOps team who carefully manage Mule application properties files.
When the Mule applications are migrated from the current customer-hosted server group to Runtime Fabric (RTF), fo the Mule applications need to be rewritten and what DevOps port configuration responsibilities change or stay the same?
See the explanation below.
* Anypoint Runtime Fabric is a container service that automates the deployment and orchestration of your Mule applications and gateways.
* Runtime Fabric runs on customer-managed infrastructure on AWS, Azure, virtual machines (VMs) or bare-metal servers.
* As none of the Mule applications use Mule domain projects. applications are not required to be rewritten. Also when applications are deployed on RTF, by default ingress is allowed only on 8081.
* Hence port conflicts are not required to be managed by DevOps team
An organization is evaluating using the CloudHub shared Load Balancer (SLB) vs creating a CloudHub dedicated load balancer (DLB). They are evaluating how this choice affects the various types of certificates used by CloudHub deplpoyed Mule applications, including MuleSoft-provided, customer-provided, or Mule application-provided certificates.
What type of restrictions exist on the types of certificates that can be exposed by the CloudHub Shared Load Balancer (SLB) to external web clients over the public internet?
See the explanation below.
https://docs.mulesoft.com/runtime-manager/dedicated-load-balancer-tutorial
A Mule application is being designed To receive nightly a CSV file containing millions of records from an external vendor over SFTP, The records from the file need to be validated, transformed. And then written to a database. Records can be inserted into the database in any order.
In this use case, what combination of Mule components provides the most effective and performant way to write these records to the database?
See the explanation below.
Correct answer is Use a Batch job scope to bulk insert records into the database
* Batch Job is most efficient way to manage millions of records.
A few points to note here are as follows :
Reliability: If you want reliabilty while processing the records, i.e should the processing survive a runtime crash or other unhappy scenarios, and when restarted process all the remaining records, if yes then go for batch as it uses persistent queues.
Error Handling: In Parallel for each an error in a particular route will stop processing the remaining records in that route and in such case you'd need to handle it using on error continue, batch process does not stop during such error instead you can have a step for failures and have a dedicated handling in it.
Memory footprint: Since question said that there are millions of records to process, parallel for each will aggregate all the processed records at the end and can possibly cause Out Of Memory.
Batch job instead provides a BatchResult in the on complete phase where you can get the count of failures and success. For huge file processing if order is not a concern definitely go ahead with Batch Job
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