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Salesforce Certified MuleSoft Integration Architect I Sample Questions (Q87-Q92):

NEW QUESTION # 87
A company is implementing a new Mule application that supports a set of critical functions driven by a rest API enabled, claims payment rules engine hosted on oracle ERP. As designed the mule application requires many data transformation operations as it performs its batch processing logic.
The company wants to leverage and reuse as many of its existing java-based capabilities (classes, objects, data model etc.) as possible What approach should be considered when implementing required data mappings and transformations between Mule application and Oracle ERP in the new Mule application?

A. Create a new metadata RAML classes in Mule from the appropriate Java objects and then perform transformations via Dataweave
B. Transform by calling any suitable Java class from Dataweave
C. Invoke any of the appropriate Java methods directly, create metadata RAML classes and then perform required transformations via Dataweave
D. From the mule application, transform via theXSLT model

Answer: B

Explanation:
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:
* Ensure the Java classes that contain the transformation logic are available in the Mule application project.
* Compile the Java classes if they are not already compiled and place the .class files or JAR file in the Mule project's classpath.
* Configure DataWeave to Call Java Methods:
* Use DataWeave's capability to invoke Java methods within the transformation scripts.
* Import the Java classes and methods in the DataWeave script.
%dw 2.0 import * from my.package.ClassName output application/json --- { transformedData: ClassName::
methodName(payload) }
* Perform Transformations:
* Write DataWeave scripts that call the appropriate Java methods to perform the necessary transformations.
* Ensure the input and output types match between DataWeave and the Java methods.
* Test Transformations:
* Thoroughly test the transformations to ensure the Java methods are correctly invoked and the expected transformations are applied.
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

NEW QUESTION # 88
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?

A. Use a Scatter-Gather to bulk insert records into the database
B. Use a Parallel for Each scope to Insert records one by one into the database
C. Use a Batch job scope to bulk insert records into the database.
D. Use a DataWeave map operation and an Async scope to insert records one by one into the database.

Answer: C

Explanation:
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

NEW QUESTION # 89
An organization plans to extend its Mule APIs to the EU (Frankfurt) region.
Currently, all Mule applications are deployed to CloudHub 1.0 in the default North American region, from the North America control plane, following this naming convention: {API-name}-{environment} (for example, Orderssapi-dev, Orders-sapi--qa, Orders-sapi--prod, etc.).
There is no network restriction to block communications between APIs.
What strategy should be implemented in order to deploy the same Mule APIs to the CloudHub 1.0 EU region from the North America control plane, as well as to minimize latency between APIs and target users and systems in Europe?

A. In Runtime Manager, for each Mule application deployment, set the Region property to EU (Frankfurt) and reuse the same Mule application mame as in the North American region.
Communicate the new urls {API-name}-{environment}.de-ci.cloudhub.io to the consuming API clients In Europe.
B. In Runtime Manager, for each Mule application deployment, leave the Region property blank (default) and change the Mule application name to {API-name}-
{environment).de-cl.
Communicate the new urls {API-name}-{environment}.de-ci1.cloudhub.io to the consuming API clients in Europe.
C. In API Manager, leave the Region property blank (default) to deploy an API proxy named {API-name}
~proxy~- (environment}.de-cl for each Mule application.
Communicate the new url {API-name}-proxy-{environment}.de-cl.cloudhub.io to the consuming API clients in Europe.
D. In API Manager, set the Region property to EU (Frankfurt) to create an API proxy named {API-name}
-proxy-{environment} for each Mule application.
Communicate the new url {API-name}-proxy-{environment}.de-c1.cloudhub.io to the consuming API clients In Europe.

Answer: A

Explanation:
To extend Mule APIs to the EU (Frankfurt) region and minimize latency for European users, follow these steps:
* Set Region Property: In Runtime Manager, for each Mule application deployment, set the Region property to EU (Frankfurt). This deploys the application to the desired region, optimizing performance for European users.
* Reuse Application Names: Keep the same Mule application names as used in the North American region. This approach maintains consistency and simplifies management.
* Communicate New URLs: Inform the consuming API clients in Europe of the new URLs in the format
{API-name}-{environment}.de-ci.cloudhub.io. These URLs will direct the clients to the applications deployed in the EU region, ensuring reduced latency and improved performance.
This strategy effectively deploys the same Mule APIs to the CloudHub EU region, leveraging the existing control plane in North America.

NEW QUESTION # 90
What is a key difference between synchronous and asynchronous logging from Mule applications?

A. Asynchronous logging can improve Mule event processing throughput while also reducing the processing time for each Mule event
B. Synchronous logging writes log messages in a single logging thread but does not block the Mule event being processed by the next event processor
C. Asynchronous logging produces more reliable audit trails with more accurate timestamps
D. Synchronous logging within an ongoing transaction writes log messages in the same thread that processes the current Mule event

Answer: A

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.

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

NEW QUESTION # 91
An organization has chosen Mulesoft for their integration and API platform.
According to the Mulesoft catalyst framework, what would an integration architect do to create achievement goals as part of their business outcomes?

A. agree upon KPI's and help develop and overall success plan
B. build and publish foundational assets
C. Measure the impact of the centre for enablement
D. evangelize API's

Answer: A

Explanation:
According to the MuleSoft Catalyst framework, an Integration Architect plays a crucial role in defining and achieving business outcomes. One of their key responsibilities is to agree upon Key Performance Indicators (KPIs) and help develop an overall success plan. This involves working with stakeholders to identify measurable goals and ensure that the integration initiatives align with the organization's strategic objectives.
KPIs are critical for tracking progress, measuring success, and making data-driven decisions. By agreeing on KPIs and developing a success plan, the Integration Architect ensures that the organization can objectively measure the impact of its integration efforts and adjust strategies as needed to achieve desired business outcomes.
References:
* MuleSoft Catalyst Knowledge Hub

NEW QUESTION # 92
......

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