MCIA-Level-1 Exam Dumps - MuleSoft Certified Integration Architect - Level 1

A canonical data model is used in integration projects to provide a standard way of representing data across different systems and applications. The two main reasons for using a canonical data model are:

A. To have a consistent data structure aligned in processes: By using a canonical data model, you ensure that data is represented uniformly across all processes and systems. This consistency simplifies data integration, reduces transformation logic, and minimizes errors.

E. Because the model isolates the back-end systems and supports Mule applications from change: A canonical data model abstracts the underlying data structures of the back-end systems. This isolation means that changes in the back-end systems do not directly affect the Mule applications. The applications interact with the canonical model, and any required transformations are handled centrally, making the system more adaptable to changes.

Implementation Steps:

• Design the Canonical Data Model:
• Implement Data Transformation Logic:
• Integrate with Mule Applications:

Using a canonical data model improves data consistency and system flexibility, making it a valuable approach in integration projects.

References:

• MuleSoft Documentation: DataWeave
• MuleSoft Documentation: Integration Best Practices

If the size of the stream exceeds the maximum, a STREAM_MAXIMUM_SIZE_EXCEEDED error is raised.

To efficiently handle the invocation of vendor applications and retry requests that generate timeout errors, the most performant way is:

• Scatter-Gather Scope: Use a Scatter-Gather scope to invoke each vendor application in parallel. This approach allows the API to send requests to both vendors simultaneously, reducing overall processing time.
• Until-Successful Scope: Inside each route of the Scatter-Gather scope, use an Until-Successful scope to handle retries. The Until-Successful scope will retry the request in case of timeout errors until the request succeeds or the maximum retry limit is reached.

This combination ensures that the API can handle intermittent network issues efficiently while minimizing redundant transactions and maximizing performance.

References

• MuleSoft Documentation on Scatter-Gather Scope
• MuleSoft Documentation on Until-Successful Scope and Error Handling

Explanation

We need to note few things about the scenario which will help us in reaching the correct solution.

Point 1 : The APIs are all publicly available and are associated with several mobile applications and web applications. This means Apply an IP blacklist policy is not viable option. as blacklisting IPs is limited to partial web traffic. It can't be useful for traffic from mobile application

Point 2 : The organization does NOT want to use any authentication or compliance policies for these APIs. This means we can not apply HTTPS mutual authentication scheme.

Header injection or removal will not help the purpose.

By its nature, JSON is vulnerable to JavaScript injection. When you parse the JSON object, the malicious code inflicts its damages. An inordinate increase in the size and depth of the JSON payload can indicate injection. Applying the JSON threat protection policy can limit the size of your JSON payload and thwart recursive additions to the JSON hierarchy.

Hence correct answer is Apply a JSON threat protection policy to all APIs to detect potential threat vectors

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 8081.

* Hence port conflicts are not required to be managed by DevOps team

Explanation

* Shared load balancers don’t allow you to configure custom SSL certificates or proxy rules

* Dedicated Load Balancer are optional but you need to purchase them additionally if needed.

* TLS is a cryptographic protocol that provides communications security for your Mule app. TLS offers many different ways of exchanging keys for authentication, encrypting data, and guaranteeing message integrity.

* The CloudHub Shared Load Balancer terminates TLS connections and uses its own server-side certificate.

* Only a DLB allows the configuration of a custom TLS server certificate

* DLB enables you to define SSL configurations to provide custom certificates and optionally enforce two-way SSL client authentication.

* To use a DLB in your environment, you must first create an Anypoint VPC. Because you can associate multiple environments with the same Anypoint VPC, you can use the same dedicated load balancer for your different environments.

* MuleSoft Reference: https://docs.mulesoft.com/runtime-manager/dedicated-load-balancer-tutorial

Additional Info on SLB Vs DLB:

Table Description automatically generated

• Requirement Analysis: The organization wants to standardize dependencies across all new and ongoing MuleSoft projects to minimize the impact of frequent plugin version upgrades and external plugin project dependencies.
• Solution: Creating a parent POM (Project Object Model) file with all required dependencies and referencing it in each application's POM.xml file is the best practice.
• Implementation Steps:

pom 1.0.0 pom org.mule.runtime mule-core-extensions-maven-plugin 1.0.0

• uk.co.certification.simulator.questionpool.PList@18be4ec0

4.0.0 com.example parent-pom 1.0.0 child-application 1.0.0

• uk.co.certification.simulator.questionpool.PList@18be4f40
• Advantages:

References

• MuleSoft Documentation on Managing dependencies with Maven
• Apache Maven Documentation on POM Reference