MuleSoft-Integration-Architect-I Dumps With Exact Questions and Answers

• MUnit and Test Recorder:
• Core Consideration:
• Error Handling in MUnit:

References:

• MuleSoft Documentation on MUnit: MUnit Documentation
• MuleSoft Blog on MUnit Test Recorder: MUnit Test Recorder

To ensure that non-functional requirements, such as rate limiting, are clearly communicated and enforced in the designed API definitions, the project team should use API fragments for the appropriate policy. Here's why option D is correct:

• API Governance and Policies: Mulesoft's API governance framework allows the definition and enforcement of policies across APIs to ensure consistency and compliance with organizational standards. These policies can include security, rate limiting, logging, and more.
• Policy Fragments: By updating API definitions with policy fragments, the team can encapsulate the non-functional requirements within the API specification itself. This approach ensures that these requirements are an integral part of the API design and are automatically applied whenever the API is deployed.
• Publishing to Exchange: Publishing the updated API definitions with the policy fragments to Anypoint Exchange makes them available for reuse and ensures that all stakeholders have access to the latest, compliant API specifications.

Example of adding a rate limiting policy fragment to a RAML file:

#%RAML 1.0 title: Example API version: v1 baseUri: https://api.example.com/v1 ... /* Include the rate limiting policy fragment */ uses: rateLimitPolicy: !include rate-limit-policy.raml

The rate-limit-policy.raml fragment might define the specific rate limiting rules as per the service level agreements.

References

• MuleSoft API Manager
• Defining and Using API Fragments

• To design an integration Mule application that processes orders and ensures reliability even with an unreliable back-end system, the following components and ActiveMQ queues should be used:

• Until Successful Scope: This scope ensures that the Mule application will continue trying to submit the order to the back-end system until it succeeds or reaches a specified retry limit. This helps in handling transient network issues or minor outages of the back-end system.
• ActiveMQ Long-Retry Queues: By placing the orders in long-retry queues, the application can manage retries over an extended period. This is particularly useful when the back-end system experiences longer outages. The ActiveMQ broker, located within the organization’s firewall, can reliably handle these queues.
• ActiveMQ Dead-Letter Queues: Orders that cannot be successfully submitted after all retry attempts should be moved to dead-letter queues. This allows for manual processing of these orders. The dead-letter queue ensures that no orders are lost and provides a clear mechanism for handling failed submissions.

Implementation Steps:

• HTTP Listener: Set up an HTTP listener to receive incoming orders.
• Immediate Acknowledgment: Immediately acknowledge the receipt of the order to the client.
• Until Successful Scope: Use the Until Successful scope to attempt submitting the order to the back-end system. Configure retry intervals and limits.
• Long-Retry Queues: Configure ActiveMQ long-retry queues to manage retries.
• Dead-Letter Queues: Set up ActiveMQ dead-letter queues for orders that fail after maximum retry attempts, allowing for manual intervention.

This approach ensures that the system can handle temporary and prolonged back-end outages while minimizing manual processing.

References:

• MuleSoft Documentation on Until Successful Scope: https://docs.mulesoft.com/mule-runtime/4.3/until-successful-scope
• ActiveMQ Documentation: https://activemq.apache.org/