MuleSoft MCIA-Level-1 Exam With Confidence Using Practice Dumps

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

Explanation

* Clustering is a group of servers or mule runtime which acts as a single unit.

* Mulesoft Enterprise Edition supports scalable clustering to provide high availability for the Mulesoft application.

* In simple terms, virtual servers composed of multiple nodes and they communicate and share information through a distributed shared memory grid.

* By default, Mulesoft ensures the High availability of applications if clustering implemented.

* Let's consider the scenario one of the nodes in cluster crashed or goes down and under maintenance. In such cases, Mulesoft will ensure that requests are processed by other nodes in the cluster. Mulesoft clustering also ensures that the request is load balanced between all the nodes in a cluster.

* Clustering is only supported by on-premise Mule runtime and it is not supported in Cloudhub.

Correct answer is External monitoring tools or log aggregators must be configured to recognize the new nodes

* Rest of the options are automatically taken care of when a new node is added in cluster.

Leveraging existing Java-based capabilities for data transformations in a Mule application can enhance efficiency and reuse. Here’s how to integrate Java classes for transformations:

• Create Java Classes:
• Configure DataWeave to Call Java Methods:

%dw 2.0 import * from my.package.ClassName output application/json --- { transformedData: ClassName::methodName(payload) }

• Perform Transformations:
• Test Transformations:

This approach allows for seamless integration of existing Java logic into Mule applications, leveraging DataWeave’s power for comprehensive data transformations.

References

• MuleSoft Documentation: DataWeave and Java Integration
• MuleSoft Documentation: Using Java with Mule