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| Certification Vendor: | MuleSoft (Salesforce) |
|---|---|
| Exam Name: | MuleSoft Certified Integration Architect - Level 1 |
| Exam Number: | MCIA-Level-1 |
| Exam Format: | Multiple choice, Scenario-based questions |
| Related Certifications: | MuleSoft Certified Developer - Level 1 MuleSoft Certified Platform Architect |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Recommended Training: | MuleSoft Training Courses |
| Exam Registration: | MuleSoft Certification Portal |
| Sample Questions: | MuleSoft MCIA-Level-1 Sample Questions |
| Exam Way: | Online proctored exam or authorized testing center (varies by region) |
| Pre Condition: | Recommended prior experience with MuleSoft Certified Developer Level 1 or equivalent integration development experience. |
| Official Syllabus URL: | https://www.mulesoft.com/certification |
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NEW QUESTION # 143
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?
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 # 144
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?
Answer: D
Explanation:
* "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
NEW QUESTION # 145
Refer to the exhibit.
An organization uses a 2-node Mute runtime cluster to host one stateless API implementation. The API is accessed over HTTPS through a load balancer that uses round-robin for load distribution.
Two additional nodes have been added to the cluster and the load balancer has been configured to recognize the new nodes with no other change to the load balancer.
What average performance change is guaranteed to happen, assuming all cluster nodes are fully operational?
Answer: C
NEW QUESTION # 146
Refer to the exhibit.
A customer is running Mule applications on Runtime Fabric for Self-Managed Kubernetes (RTF-BYOKS) in a multi-cloud environment.
Based on this configuration, how do Agents and Runtime Manager
communicate, and what Is exchanged between them?
Answer: C
Explanation:
In the context of Mule applications running on Runtime Fabric for Self-Managed Kubernetes (RTF-BYOKS) in a multi-cloud environment, understanding the thread pools used for communication between Agents and Runtime Manager is crucial:
* Shared NIO Selector Pool: This pool is responsible for handling non-blocking IO operations, such as network communication. It ensures efficient handling of IO operations by using a small number of threads to manage multiple IO tasks simultaneously.
* CPU_LITE: This thread pool is used for lightweight CPU operations. It is designed to handle tasks that do not require significant computational resources, ensuring that lightweight operations are processed efficiently without overwhelming the system.
The combination of the Shared NIO Selector Pool and CPU_LITE thread pool ensures efficient and reliable communication between Agents and Runtime Manager in the RTF environment.
References:
* MuleSoft Threading and Thread Pools
* Runtime Fabric Architecture
NEW QUESTION # 147
A team has completed the build and test activities for a Mule application that implements a System API for its application network.
Which Anypoint Platform component should the team now use to both deploy and monitor the System AP\ implementation?
Answer: A
Explanation:
After completing the build and test activities for a Mule application that implements a System API, the team should use Runtime Manager to both deploy and monitor the System API implementation. Runtime Manager is a component of Anypoint Platform that provides capabilities for deploying, managing, and monitoring Mule applications and APIs. It allows teams to manage applications and APIs across various environments, ensuring they operate correctly and efficiently.
References:
* Anypoint Runtime Manager
* Deploying and Managing Applications with Runtime Manager
NEW QUESTION # 148
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