MCIA-Level-1 Exam Dumps - MuleSoft Certified Architect Questions and Answers

Explanation

•Scatter Gather: When running within a transaction, Scatter Gather does not execute in parallel. This means that the second route is executed after the first one is processed, the third after the second one, etc. In case of error, all routes will be rolled back

Explanation

Mule 4 is built to:

•Minimize the need for custom code.

•Avoid the need for you to know or understand Java.

However, some advanced uses cases require integration with custom Java code, such as:

•Reuse of a library, such as a tax calculation library.

•Reuse of a canonical object model that is standard in the organization.

•Execution of custom logic using Java.

Mule ref doc : https://docs.mulesoft.com/java-module/1.2/

Explanation

* 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

The Kubernetes controller is implementing horizontal scalability by adding another pod replica in response to increased application load. Horizontal scaling involves adding more instances of a service or application to handle the increased load, as opposed to vertical scaling, which would involve adding more resources (CPU, RAM) to a single instance. Horizontal scaling is a key feature of cloud-native applications and is supported by Kubernetes to ensure high availability and performance.

References:

• Kubernetes Horizontal Pod Autoscaling
• Understanding Horizontal vs. Vertical Scaling

• Mule Tracing Module and MDC:
• Set Logging Variable Operations:
• Configuring log4j2.xml:
• Improving Throughput and Lowering Latency:

References:

• MuleSoft Documentation on Mule Tracing: Mule Tracing Module
• Log4j2 Documentation on MDC: Log4j2 MDC

In a clustered Mule runtime environment, when using a JMS listener to consume messages from a queue destination, it is essential to ensure that messages are appropriately received by all nodes in the cluster. The configuration must support high availability and scalability. Here's why option B is correct:

• primaryNodeOnly="false": Setting this parameter to "false" ensures that the JMS listener is active on all nodes in the cluster, not just the primary node. This setting allows multiple instances of the JMS listener to run concurrently across different nodes, enabling them to consume messages from the JMS queue.
• Shared Subscription: Using a shared subscription means that all nodes will share the consumption of messages from the queue. This approach prevents duplicate message processing, as each message is delivered to only one listener instance within the cluster. This configuration ensures that message processing is balanced across the nodes, improving throughput and reliability.

To configure the JMS listener in Mule, the XML configuration might look something like this:

xml

This setup ensures that all nodes in the cluster are involved in message processing, leveraging the high availability and load balancing capabilities of the cluster.

References

• MuleSoft Documentation on JMS Listener
• MuleSoft Clustering Guide

Explanation

* Setting a transaction timeout for the Bitronix transaction manager

● Set the transaction timeout either

– In wrapper.conf

– In CloudHub in the Properties tab of the Mule application deployment

● The default is 60 secs. It is defined as

mule.bitronix.transactiontimeout = 120

* This property defines the timeout for each transaction created for this manager.

If the transaction has not terminated before the timeout expires it will be automatically rolled back.

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Additional Info around Transaction Management:

Bitronix is available as the XA transaction manager for Mule applications

● To use Bitronix, declare it as a global configuration element in the Mule application

● Each Mule runtime can have only one instance of a Bitronix transaction manager, which is shared by all Mule applications

● For customer-hosted deployments, define the XA transaction manager in a Mule domain

– Then share this global element among all Mule applications in the Mule runtime

Graphical user interface, table Description automatically generated with medium confidence

To design a caching strategy that queries the database only when there is an update to the Employees table, follow these steps:

• On Table Row Operation: Configure an On Table Row operation to monitor the Employees table for updates.
• Invalidate Cache: When the On Table Row operation detects an update, trigger the invalidate cache operation to clear the cache.
• Object Store Caching Strategy: Use an object-store-caching-strategy to store the cached responses.
• Default Expiration Interval: Use the default expiration interval for the cache, which ensures that cached data is invalidated based on the detection of changes in the Employees table rather than a fixed time interval.

This strategy ensures that the cache is only invalidated when there are actual changes in the Employees table, thereby minimizing redundant transactions and optimizing database performance.

References

• MuleSoft Documentation on Caching Strategies
• Best Practices for Database Integration with MuleSoft