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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.
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:
Table Description automatically generated
An organization is designing an integration solution to replicate financial transaction data from a legacy system into a data warehouse (DWH).
The DWH must contain a daily snapshot of financial transactions, to be delivered as a CSV file. Daily transaction volume exceeds tens of millions of records, with significant spikes in volume during popular shopping periods.
What is the most appropriate integration style for an integration solution that meets the organization's current requirements?
Correct answer is Batch-triggered ETL Within a Mule application, batch processing provides a construct for asynchronously processing larger-than-memory data sets that are split into individual records. Batch jobs allow for the description of a reliable process that automatically splits up source data and stores it into persistent queues, which makes it possible to process large data sets while providing reliability. In the event that the application is redeployed or Mule crashes, the job execution is able to resume at the point it stopped.
An integration Mute application consumes and processes a list of rows from a CSV file. Each row must be read from the CSV file, validated, and the row data sent to a JMS queue, in the exact order as in the CSV file.
If any processing step for a row falls, then a log entry must be written for that row, but processing of other rows must not be affected.
What combination of Mute components is most idiomatic (used according to their intended purpose) when Implementing the above requirements?
* On Error Propagate halts execution and sends error to the client. In this scenario it's mentioned that "processing of other rows must not be affected" so Option B and C are ruled out.
* Scatter gather is used to club multiple responses together before processing. In this scenario, we need sequential processing. So option A is out of choice.
* Correct answer is For Each scope & On Error Continue scope Below requirement can be fulfilled in the below way
1) Using For Each scope , which will send each row from csv file sequentially. each row needs to be sent sequentially as requirement is to send the message in exactly the same way as it is mentioned in the csv file
2) Also other part of requirement is if any processing step for a row fails then it should log an error but should not affect other record processing . This can be achieved using On error Continue scope on these set of activities. so that error will not halt the processing. Also logger needs to be added in error handling section so that it can be logged.
* Attaching diagram for reference. Here it's try scope, but similar would be the case with For Each loop.
Diagram Description automatically generated
An organization has various integrations implemented as Mule applications. Some of these Mule applications are deployed to custom hosted Mule runtimes (on-premises) while others execute in the MuleSoft-hosted runtime plane (CloudHub). To perform the Integra functionality, these Mule applications connect to various backend systems, with multiple applications typically needing to access the backend systems.
How can the organization most effectively avoid creating duplicates in each Mule application of the credentials required to access the backend systems?
* "Create a Mule domain project that maintains the credentials as Mule domain-shared resources" is wrong as domain project is not supported in Cloudhub * We should Avoid Creating duplicates in each Mule application but below two options cause duplication of credentials - Store the credentials in properties files in a shared folder within the organization’s data center. Have the Mule applications load properties files from this shared location at startup - Segregate the credentials for each backend system into environment-specific properties files. Package these properties files in each Mule application, from where they are loaded at startup So these are also wrong choices * Credentials service is the best approach in this scenario. Mule domain projects are not supported on CloudHub. Also its is not recommended to have multiple copies of configuration values as this makes difficult to maintain Use the Mule Credentials Vault to encrypt data in a .properties file. (In the context of this document, we refer to the .properties file simply as the properties file.) The properties file in Mule stores data as key-value pairs which may contain information such as usernames, first and last names, and credit card numbers. A Mule application may access this data as it processes messages, for example, to acquire login credentials for an external Web service. However, though this sensitive, private data must be stored in a properties file for Mule to access, it must also be protected against unauthorized – and potentially malicious – use by anyone with access to the Mule application
Mule application A receives a request Anypoint MQ message REQU with a payload containing a variable-length list of request objects. Application A uses the For Each scope to split the list into individual objects and sends each object as a message to an Anypoint MQ queue.
Service S listens on that queue, processes each message independently of all other messages, and sends a response message to a response queue.
Application A listens on that response queue and must in turn create and publish a response Anypoint MQ message RESP with a payload containing the list of responses sent by service S in the same order as the request objects originally sent in REQU.
Assume successful response messages are returned by service S for all request messages.
What is required so that application A can ensure that the length and order of the list of objects in RESP and REQU match, while at the same time maximizing message throughput?
Correct answer is Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU Explanation : Using Anypoint MQ, you can create two types of queues: Standard queue These queues don’t guarantee a specific message order. Standard queues are the best fit for applications in which messages must be delivered quickly. FIFO (first in, first out) queue These queues ensure that your messages arrive in order. FIFO queues are the best fit for applications requiring strict message ordering and exactly-once delivery, but in which message delivery speed is of less importance Use of FIFO queue is no where in the option and also it decreased throughput. Similarly persistent object store is not the preferred solution approach when you maximizing message throughput. This rules out one of the options. Scatter Gather does not support ObjectStore. This rules out one of the options. Standard Anypoint MQ queues don’t guarantee a specific message order hence using another for each block to collect response wont work as requirement here is to ensure the order. Hence considering all the above factors the feasible approach is Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU