Free Practice Questions for MuleSoft MCIA-Level-1 Exam

The interaction composition pattern used by the integration architect for the order processing API that coordinates stateful interactions with various microservices is called orchestration. Orchestration involves managing and coordinating multiple services to achieve a specific business process or workflow. In this case, the order processing API is responsible for validating, creating, and fulfilling new product orders by coordinating interactions with different microservices.

Orchestration provides a centralized control mechanism where the order processing API acts as the orchestrator, handling the sequence of service calls, managing state, and ensuring that the business process is executed correctly.

References

MuleSoft API-led Connectivity Approach

Patterns for Microservices and API Design

A. Implement HTTP caching policy for all GET endpoints for the master lookup API and implement locking to synchronize access to object store

Comprehensive Detailed Step by Step ExplanationTo resolve the performance issue observed during the performance testing of the Master lookup API, the best approach involves caching and synchronization:

HTTP Caching Policy:

Purpose: Reduces the load on the database by caching the responses for GET requests. Once a response is cached, subsequent requests for the same resource can be served from the cache instead of querying the database.

Implementation: Apply the HTTP caching policy to all GET endpoints of the Master lookup API. This policy ensures that the frequently accessed master lookup data is stored in the cache and served quickly.

Benefits: This significantly reduces the number of database queries, thus lowering the response time and improving performance.

Object Store Locking:

Purpose: Ensures data consistency and prevents race conditions when multiple workers attempt to access or update the cache concurrently.

Implementation: Use a locking mechanism to synchronize access to the object store. This can be achieved by using MuleSoft’s Object Store Connector with lock/unlock operations.

Benefits: Prevents multiple database queries for the same key and ensures that only one worker updates the cache at a time, thus avoiding redundant operations and maintaining data integrity.

By implementing these two strategies, the response time of the Master lookup API will be significantly improved, and the performance issue will be resolved.

References

MuleSoft HTTP Caching Policy

MuleSoft Object Store Connector

To automate the promotion and deployment of API Manager policies as part of a CI/CD process, an organization can use the Anypoint CLI (Command Line Interface). The Anypoint CLI provides a set of commands to interact with various components of the Anypoint Platform, including API Manager.

Using the Anypoint CLI, you can:

Apply policies to API instances.

Promote APIs and their associated policies across different environments.

Script these actions as part of the CI/CD pipeline to ensure consistency and automation in managing API policies.

Other tools like the MUnit Maven plugin, Mule Maven plugin, and Runtime Manager agent do not provide direct capabilities to manage API Manager policies.

References

MuleSoft Anypoint CLI Documentation

Automating API Management with Anypoint CLI

Try-Catch Scope for Data Fetching:

Implement a try-catch scope to handle any errors that may occur while fetching account balances from the backend application. This ensures that the main process flow can handle exceptions gracefully without failing entirely.

Async Scope for Inserting Records into Audit Database:

Use the Async scope within the try-catch block to insert records into the audit database. The Async scope allows operations to be performed asynchronously, ensuring that the insertion process does not block the main thread of execution.

By doing this, the primary flow of fetching account balances remains unaffected and can achieve higher throughput as the auditing process runs concurrently.

Ensuring Better Throughput:

The async processing ensures that the time-consuming database insertion for auditing purposes does not impede the performance of the main flow, which is crucial for real-time or near-real-time systems.

References:

MuleSoft Documentation on Async Scope

Best practices for Error Handling in MuleSoft

Anypoint Platform offers universal API management and Integration-Platform-as-a-Service (iPaaS) capabilities in a unified platform, meaning that it provides a single control plane to manage both full-lifecycle API management and integration. This allows organizations to easily manage their APIs and integrations, as well as deploy APIs and integrations quickly and efficiently. According to the MuleSoft Certified Integration Architect - Level 1 Course Book, “Anypoint Platform provides a unified platform for managing, deploying, and monitoring both API and integration solutions, allowing organizations to quickly and easily build and manage their APIs and integrations.”

When a batch job instance running on a runtime fabric environment with two cluster replicas encounters an unexpected failure, the following sequence of events occurs:

Failure Detection: The runtime fabric environment detects the failure of the replica running the batch job.

Replica Replacement: A new replica is provisioned to replace the failed one.

Batch Job State Recovery: MuleSoft’s batch processing mechanism ensures that the state of the batch job is periodically saved (checkpointing). This allows the new replica to resume processing from the last saved state.

Resumption of Processing: The new replica takes over the batch job, continuing from where the previous one left off, thus processing the remaining 99,000 records without starting from scratch or causing duplicate processing.

References:

MuleSoft Documentation on Batch Processing

Runtime Fabric Guide

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The mocking service is a feature of Anypoint Platform and runs continuously. You can run the mocking service from the text editor, the visual editor, and from Anypoint Exchange. You can simulate calls to the API in API Designer before publishing the API specification to Exchange or in Exchange after publishing the API specification.

A major distinguishing characteristic of an application network, according to MuleSoft, is that it is built for change and self-service. An application network connects applications, data, and devices with APIs, enabling self-service access and reuse of assets. This architecture allows organizations to rapidly adapt to changing business needs, fosters innovation, and reduces time to market by empowering different teams to access and integrate systems independently without waiting for centralized IT.

References:

Application Networks: The Future of Integration

MuleSoft's Approach to Building Application Networks

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/

Explanation

Correct answer is Implement transformation logic in the new Mule application using DataWeave, invoking existing transformation services when possible. * The key here minimal testing effort, "Extend existing transformation logic" is not a feasible option because additional functionality is highly specific to the new Mule application so it should not be a part of commonly used functionality. So this option is ruled out. * "Implement transformation logic in the new Mule application using DataWeave, replicating the transformation logic of existing transformation services" Replicating the transformation logic of existing transformation services will cause duplicity of code. So this option is ruled out. * "Implement and expose all transformation logic as microservices using DataWeave, so it can be reused by any application component that needs it, including the new Mule application" as question specifies that the transformation is app specific and wont be used outside