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Latest MCIA-Level 1 MAINTENANCE Exam Dumps Questions
The dumps for MCIA-Level 1 MAINTENANCE exam was last updated on Apr 23,2025 .
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What is a key difference between synchronous and asynchronous logging from Mule applications?
Explanation: Types of logging: A) Synchronous: The execution of thread that is processing messages is interrupted to wait for the log message to be fully handled before it can continue. The execution of the thread that is processing your message is interrupted to wait for the log message to be fully output before it can continue Performance degrades because of synchronous logging Used when the log is used as an audit trail or when logging ERROR/CRITICAL messages If the logger fails to write to disk, the exception would raise on the same thread that's currently processing the Mule event. If logging is critical for you, then you can rollback the transaction. Chart, diagram Description automatically generated Chart, diagram, box and whisker chart Description automatically generated B) Asynchronous: The logging operation occurs in a separate thread, so the actual processing of your message won’t be delayed to wait for the logging to complete Substantial improvement in throughput and latency of message processing Mule runtime engine (Mule) 4 uses Log4j 2 asynchronous logging by default The disadvantage of asynchronous logging is error handling. If the logger fails to write to disk, the thread doing the processing won't be aware of any issues writing to the disk, so you won't be able to rollback anything. Because the actual writing of the log gets differed, there's a chance that log messages might never make it to disk and get lost, if Mule were to crash before the buffers are flushed. ------------------------------------------------------------------------------------------------------------------ So Correct answer is: Asynchronous logging can improve Mule event processing throughput while also reducing the processing time for each Mule event
A finance giant is planning to migrate all its Mule applications to Runtime fabric (RTF). Currently all Mule applications are deployed cloud hub using automated CI/CD scripts. As an integration architect, which of the below step would you suggest to ensure that the applications from cloudhub are migrated properly to Runtime Fabric (RTF) with an assumption that organization is keen on keeping the same deployment strategy.
A leading e-commerce giant will use Mulesoft API's on runtime fabric (RTF) to process customer orders. Some customer's sensitive information such as credit card information is also there as a part of a API payload. What approach minimizes the risk of matching sensitive data to the original and can convert back to the original value whenever and wherever required?
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.
Explanation: 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 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?
Explanation: * 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 808 1. * Hence port conflicts are not required to be managed by DevOps team
Exam Code: MCIA-Level 1 MAINTENANCE Q & A: 116 Q&As Updated: Apr 23,2025
