MuleSoft-Integration-Architect-I Sample Questions Answers

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.

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

ActiveMQ Documentation: https://activemq.apache.org/

Dealer D1 - AUTO Acknowledgment:

Dealer D1, which has a retry mechanism, can use AUTO acknowledgment. This means that the message is acknowledged automatically once received. The retry mechanism will handle any processing errors and reprocess the message if needed.

Dealer D2 - Manual Acknowledgment:

Dealer D2, which does not have a retry mechanism, must use manual acknowledgment. This approach allows D2 to acknowledge the message only after it has been successfully processed, ensuring no message is lost due to processing errors.

Ensuring Message Delivery:

Using manual acknowledgment for Dealer D2 ensures that the message is not lost and can be reprocessed if an error occurs during processing, which is crucial since it lacks a retry mechanism.

Explanation

* "Relocation of the database systems to a DMZ in the on-premises network, with Mule applications deployed to the CloudHub Shared Worker Cloud connecting only to the DMZ" is not a feasible option

* "Static IP addresses for the Mule applications deployed to the CloudHub Shared Worker Cloud, plus matching firewall rules and IP whitelisting in the on-premises network" - It is risk for sensitive data. - Even if you whitelist the database IP on your app, your app wont be able to connect to the database so this is also not a feasible option

* "An Anypoint VPC with one Dedicated Load Balancer fronting each on-premises database system, plus matching IP whitelisting in the load balancer and firewall rules in the VPC and on-premises network" Adding one VPC with a DLB for each backend system also makes no sense, is way too much work. Why would you add a LB for one system.

* Correct answer: "An Anypoint VPC connected to the on-premises network using an IPsec tunnel or AWS DirectConnect, plus matching firewall rules in the VPC and on-premises network"

IPsec Tunnel You can use an IPsec tunnel with network-to-network configuration to connect your on-premises data centers to your Anypoint VPC. An IPsec VPN tunnel is generally the recommended solution for VPC to on-premises connectivity, as it provides a standardized, secure way to connect. This method also integrates well with existing IT infrastructure such as routers and appliances. Reference: https://docs.mulesoft.com/runtime-manager/vpc-connectivity-methods-concept

Diagram Description automatically generated

Explanation

* Organization should continue to use SCM system of their choice, in addition to keeping source code for these asset types in Anypoint Exchange, thereby enabling parallel development, branching.

* Reason is that Anypoint exchange is not full fledged version repositories like GitHub.

* But at same time it is tightly coupled with Mule assets

Explanation

Below Anypoint Connectors support transactions ● JMS – Publish – Consume ● VM – Publish – Consume ● Database – All operations

To close the IT delivery gap, MuleSoft recommends that IT organizations make their technology assets easily discoverable via a central repository. This approach ensures that existing assets, such as APIs, connectors, templates, and other reusable components, are accessible to all teams within the organization. By having a central repository, teams can quickly find and leverage these assets, reducing duplication of effort, speeding up project delivery, and fostering a culture of reuse and collaboration. This strategy aligns with the principles of an API-led connectivity approach, where reusable assets and APIs form the foundation for scalable and efficient IT delivery.

References

MuleSoft Documentation on API-led Connectivity

Anypoint Platform Best Practices for Asset Management

Explanation

Mule applications are deployed in CloudHub workers and each worker is assigned with a dedicated IP • For zero downtime deployment, each worker in CloudHub needs additional IP addresses • A few IPs in a VPC are reserved for infrastructure (generally 2 IPs) • The IP addresses are usually in a private range with a subnet block specifier, such as 10.0.0.1/24 • The smallest CIDR network subnet block you can assign for your VPC is /24 (256 IP addresses) (60*3 env * 2 worker per application ) + 50% of (total) for zero downtime = 540 In this case correct answer is 10.0.0.0/22 as this provided 1024 IP's .

Other IP's are insufficient.

Set the Anypoint MQ connector operation to publish or consume messages, or to accept (ACK) or not accept (NACK) a message.

To automate the API lifecycle in a CI/CD pipeline efficiently, leveraging Anypoint Platform's capabilities is crucial. Anypoint CLI (Command Line Interface) and Anypoint Platform REST APIs provide robust tools for managing various aspects of the API lifecycle, such as publishing, sharing, discovering, registering, applying policies, and deploying APIs. By using these tools with a scripting language like Groovy, you can script and automate these tasks to reduce manual intervention, ensuring consistency and efficiency.

Anypoint CLI allows you to interact with the Anypoint Platform from the command line, enabling automated deployments, management of APIs, and configuration of policies. The Anypoint Platform REST APIs provide comprehensive programmatic access to the platform’s functionalities, allowing for seamless integration into CI/CD pipelines. By combining these with a scripting language, you can create scripts that automate repetitive tasks, streamline processes, and ensure that your API lifecycle management is both efficient and reliable.

MuleSoft Documentation on Anypoint CLI

MuleSoft Documentation on Anypoint Platform REST APIs

To achieve efficient caching and reduce redundant database transactions, the following strategy can be implemented:

On Table Row Listener: Implement an "On Table Row" trigger on the employees' table. This trigger will monitor changes (inserts, updates, deletes) in the employee records.

Invalidate Cache: Upon detecting changes in the employees' table, the trigger will call a flow to invalidate the current cache.

Object Store for Caching: Utilize MuleSoft's object store to cache the employee data. This store can hold the data for quick retrieval.

Set Expiration Interval: Configure the expiration interval for the cached data to ensure it is cleared when necessary. For this scenario, since we are invalidating cache on actual data changes, setting the expiration interval to empty can be suitable.

Return Cached Data: If there are no updates, the cached response is returned, reducing database load.

MuleSoft Documentation on Object Store

Caching Strategies

Explanation

By default, CloudHub replaces a Mule application's log4j2.xml file with a CloudHub log4j2.xml file. In CloudHub, you can disable the CloudHub provided Mule application log4j2 file. This allows integrating Mule application logs with custom or third-party log management systems

Parallel Execution:

In a Scatter-Gather scope, all routes are executed in parallel. This allows multiple operations to be performed simultaneously, which can improve performance and efficiency when dealing with independent tasks.

Transaction Management:

When Scatter-Gather operates within a local transaction, all database operations performed in parallel routes are part of the same transaction context.

Error Handling and Rollback:

If any error occurs within any of the parallel routes inside the Scatter-Gather scope, it triggers a rollback of all the database operations performed within the transaction. This ensures data consistency and integrity.

Explanation

* If we store Correlation id value in step 1 as Mule event variables/attributes, the values will be cleared after server restart and we want system to be fault tolerant.

* The Correlation ID value in Step 1 should be stored in a persistent object store.

* We don't need to store Correlation ID value in Step 3 to persistent object store. We can store it but as we also need to make application performant. We can avoid this step of accessing persistent object store.

*  Accessing persistent object stores slow down the performance as persistent object stores are by default stored in shared file systems.

*  As the SOAP service is idempotent in nature. In case of any failures , using this Correlation ID saved in first step we can make call to SOAP service and validate the Correlation ID.

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Additional Information:

* Competing Consumers are multiple consumers that are all created to receive messages from a single Point-to-Point Channel. When the channel delivers a message, any of the consumers could potentially receive it. The messaging system's implementation determines which consumer actually receives the message, but in effect the consumers compete with each other to be the receiver. Once a consumer receives a message, it can delegate to the rest of its application to help process the message.

* In case you are unaware about term idempotent re is more info:

Idempotent operations means their result will always same no matter how many times these operations are invoked.

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The MuleSoft-recommended best practice for sharing the connector and configuration information among multiple APIs is to use a Mule domain project. The steps are:

Create a Mule domain project.

Add the Database connector and its configuration to the domain project.

Reference this domain project from each System API that needs to use the Database connector and configuration.

By using a domain project, you centralize the configuration and reuse it across multiple APIs. This approach ensures consistency, reduces duplication, and simplifies maintenance and updates to the connector configuration.

References

MuleSoft Documentation on Domain Projects

Best Practices for Reusable Configuration in MuleSoft

In a microservices architecture, a service mesh manages the communication between microservices. This involves handling service discovery, load balancing, failure recovery, metrics, and monitoring. Service meshes also provide more complex operational requirements like A/B testing, canary releases, rate limiting, access control, and end-to-end authentication. By abstracting these functionalities away from individual microservices, a service mesh allows developers to focus on business logic while ensuring reliable and secure inter-service communication.

To ensure that data is encrypted both in-transit and at-rest, and to validate incoming requests to the Payment Processing API, the following approach is recommended:

TLS Inbound Policy: Apply a Transport Layer Security (TLS) - Inbound policy in API Manager. This policy ensures that the data is encrypted during transmission and can be decrypted by the API Manager before it reaches the Mule application.

Decryption: With the TLS policy applied, the message payload is decrypted when it is received by the API Manager.

JSON Validation: After decryption, the Mule application can use the JSON Validation module to validate the structure and content of the JSON data. This ensures that the payload conforms to the specified format and contains valid data.

This approach ensures that data is securely transmitted and properly validated upon receipt.

Transport Layer Security (TLS) Policies

JSON Validation Module

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.

MuleSoft Threading and Thread Pools

Runtime Fabric Architecture

Explanation

* On Error Propagate halts execution and sends error to the client. In this scenario it's mentioned that "processing of other rows must not be affected" so Option B and C are ruled out.

* Scatter gather is used to club multiple responses together before processing. In this scenario, we need sequential processing. So option A is out of choice.

* Correct answer is For Each scope & On Error Continue scope Below requirement can be fulfilled in the below way

1) Using For Each scope , which will send each row from csv file sequentially. each row needs to be sent sequentially as requirement is to send the message in exactly the same way as it is mentioned in the csv file

2) Also other part of requirement is if any processing step for a row fails then it should log an error but should not affect other record processing . This can be achieved using On error Continue scope on these set of activities. so that error will not halt the processing. Also logger needs to be added in error handling section so that it can be logged.

* Attaching diagram for reference. Here it's try scope, but similar would be the case with For Each loop.

REST (Representational State Transfer) is an architectural style for designing networked applications. It supports multiple content types, including XML and JSON, making it suitable for APIs that need to handle requests from clients in both formats. RESTful APIs use standard HTTP methods (GET, POST, PUT, DELETE) and are designed to be stateless and scalable. REST's flexibility and wide adoption make it an appropriate choice for the integration architect’s requirements.

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

Explanation

* An object store is a facility for storing objects in or across Mule applications. Mule uses object stores to persist data for eventual retrieval.

* Mule provides two types of object stores:

1) In-memory store – stores objects in local Mule runtime memory. Objects are lost on shutdown of the Mule runtime.

2) Persistent store – Mule persists data when an object store is explicitly configured to be persistent.

In a standalone Mule runtime, Mule creates a default persistent store in the file system. If you do not specify an object store, the default persistent object store is used.

MuleSoft Reference: https://docs.mulesoft.com/mule-runtime/3.9/mule-object-stores

To monitor Mule applications deployed on customer-hosted Mule runtimes from Anypoint Platform, you need to enable monitoring for each application within the Runtime Manager's application settings. This step ensures that the Runtime Manager Agent, which is already part of the Mule runtime, will collect and send monitoring data to Anypoint Monitoring.

The Runtime Manager Agent acts as the intermediary, collecting metrics and operational data from the Mule applications and transmitting this data to Anypoint Monitoring for analysis and visualization. This setup provides real-time insights and allows for effective monitoring and management of Mule applications.

MuleSoft Documentation on Anypoint Monitoring

Explanation

•Scatter Gather: When running within a transaction, Scatter Gather does not execute in parallel. This means that the second route is executed after the first one is processed, the third after the second one, etc. In case of error, all routes will be rolled back

Explanation

Correct answer is Untill Successful scope can be implemented while calling backend API's The Until Successful scope repeatedly triggers the scope's components (including flow references) until they all succeed or until a maximum number of retries is exceeded The scope provides option to control the max number of retries and the interval between retries The scope can execute any sequence of processors that may fail for whatever reason and may succeed upon retry

When an API client makes an HTTP request to an API gateway with an Accept header containing the value "application/json", the valid HTTP response payload should be in JSON format. The correct JSON format for indicating a healthy status is {"status": "healthy"}. This format uses a JSON object with a key-value pair where the key is "status" and the value is "healthy".

Other options provided are not valid JSON responses:

healthy is XML format.

status('healthy') and status: healthy are not valid JSON syntax.

References

HTTP Content Negotiation and Accept Headers

JSON Formatting and Syntax Rules

MUnit and Test Recorder:

MUnit is MuleSoft's testing framework designed to create automated tests for Mule applications.

Test Recorder in Anypoint Studio allows capturing live processing flows and events to automatically generate test cases.

Core Consideration:

While using Test Recorder, it is essential to consider how errors within the flows are handled.

If Mule errors are raised within the flows, Test Recorder will not be able to create tests for these flows, even if the errors are managed by On-Error Continue error handlers.

This limitation requires careful design and error handling strategies to ensure test cases can be recorded and executed effectively.

Error Handling in MUnit:

MUnit allows simulating error scenarios using mocks and spies.

Proper error handling within the application flow ensures robustness and reliable test creation.

However, the inability to create tests when errors are raised during recording indicates a need for alternative approaches to manage errors during test case generation.

MuleSoft Documentation on MUnit: MUnit Documentation

MuleSoft Blog on MUnit Test Recorder: MUnit Test Recorder

To use a dedicated load balancer 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.

In MuleSoft's Anypoint Exchange, a complete API specification in RAML (RESTful API Modeling Language) or OAS (OpenAPI Specification) format is represented as a "REST API". This asset type provides a comprehensive description of the API, including endpoints, methods, request/response structures, and other relevant details. It serves as the blueprint for implementing and consuming the API, ensuring that all stakeholders have a clear understanding of its functionality and design.

Other asset types like connectors, API spec fragments, and SOAP APIs represent different components or partial specifications, but only REST APIs provide the full specification in the mentioned formats.

References

MuleSoft Anypoint Exchange Documentation

API Specification Documentation on RAML and OAS

Mule Tracing Module and MDC:

The Mule Tracing module enhances logging by adding context-specific information to logs using Mapped Diagnostic Context (MDC).

MDC allows including additional metadata in logs, making it easier to trace and debug issues in the application.

Set Logging Variable Operations:

In Mule flows, before any Core Logger components, add Set logging variable operations from the Mule Tracing module.

This ensures that the contextual information is set before logs are written.

Configuring log4j2.xml:

Update the log4j2.xml file to change the appender's pattern layout to include %MDC placeholders.

Assign the appender to either a Logger or the Root element to ensure that logs across the application include the MDC-enriched information.

Improving Throughput and Lowering Latency:

Using MDC with %MDC placeholders ensures that enriched logging does not significantly impact performance.

This setup improves logging detail while maintaining efficient message processing.

MuleSoft Documentation on Mule Tracing: Mule Tracing Module

Log4j2 Documentation on MDC: Log4j2 MDC

Explanation

* Any change in the application will require a restart except when the issue outside the app. For below situations , you would need to redeploy the code after doing necessary changes

-- One of the stored procedures being called by the Mule application has been renamed. In this case, in the Mule application you will have to do changes to accommodate the new stored procedure name.

-- Required redesign of Mule applications to follow microservice architecture principles. As code is changed, deployment is must

-- If the credentials changed and you need to update the connector or the properties.

-- The credentials for accessing the database have been updated and the previous credentials are no longer valid. In this situation you need to restart or redeploy depending on how credentials are configured in Mule application.

* So Correct answer is The database server was unavailable for four hours due to a major outage but is now fully operational again as this is the only external issue to application.

MUnit is the testing framework component of Anypoint Platform that provides test automation capabilities for customers to use in their CI/CD pipelines. MUnit allows developers to write, run, and automate unit and integration tests for Mule applications. It supports creating repeatable tests for various scenarios, ensuring that the application functions correctly and adheres to specified requirements. MUnit integrates seamlessly with Anypoint Studio, enabling efficient test creation and execution as part of the development lifecycle.

Explanation

An SLA for the upstream API CANNOT be provided.

Explanation

Correct approach is to use Mixed configuration of asynchronous or synchronous loggers shoud be used to log exceptions via synchronous way Asynchronous logging poses a performance-reliability trade-off. You may lose some messages if Mule crashes before the logging buffers flush to the disk. In this case, consider that you can have a mixed configuration of asynchronous or synchronous loggers in your app. Best practice is to use asynchronous logging over synchronous with a minimum logging level of WARN for a production application. In some cases, enable INFO logging level when you need to confirm events such as successful policy installation or to perform troubleshooting. Configure your logging strategy by editing your application’s src/main/resources/log4j2.xml file

To support the company's goals of unlocking key systems and data, quickly deploying scalable APIs, and connecting to on-premises systems, while also preparing for future migrations, the best runtime deployment option is using Runtime Fabric on self-managed Kubernetes. Here's why:

Scalability: Kubernetes is designed to scale applications easily and efficiently. By deploying Mule runtimes on a self-managed Kubernetes cluster, the company can dynamically scale its APIs based on demand, ensuring performance and reliability.

Flexibility: Running on self-managed Kubernetes allows the company to have control over the infrastructure. They can customize the environment to meet their specific needs, integrate with existing on-premises systems, and support their microservices architecture running in Docker containers.

Future-Proofing: Kubernetes supports hybrid and multi-cloud deployments, making it easier to migrate workloads between different environments as needed. This aligns with the company's goal of being able to migrate their systems as part of their strategic approach.

Efficiency: By leveraging Kubernetes, the company can automate deployment, scaling, and management of containerized applications, reducing the burden on IT and DevOps teams.

MuleSoft Documentation on Runtime Fabric: https://docs.mulesoft.com/runtime-fabric/1.9/

Kubernetes Documentation: https://kubernetes.io/docs/home/

Explanation

* If you need to scale a JMS provider/ message broker, - add nodes to scale it horizontally or - add memory to scale it vertically * Cons of adding another JMS provider/ message broker: - adds cost. - adds complexity to use two JMS brokers - adds Operational overhead if we use two brokers, say, ActiveMQ and IBM MQ * So Two options that mention to use two brokers are not best choice. * It's mentioned that "The Fulfillment microservice consumes Order messages, fulfills the order described therein, and then publishes an OrderFulfilled message. Each OrderFulfilled message can be consumed by any interested Mule application." - When you publish a message on a topic, it goes to all the subscribers who are interested - so zero to many subscribers will receive a copy of the message. - When you send a message on a queue, it will be received by exactly one consumer. * As we need multiple consumers to consume the message below option is not valid choice: "Order messages are sent to an Anypoint MQ exchange. OrderFulfilled messages are sent to an Anypoint MQ queue. Both microservices interact with Anypoint MQ as the message broker, which must therefore scale to support the load of both microservices" * Order messages are only consumed by one Mule application, the Fulfillment microservice, so we will publish it on queue and OrderFulfilled message can be consumed by any interested Mule application so it need to be published on Topic using same broker. * Correct Answer: Best choice in this scenario is: "Order messages are sent to a JMS queue. OrderFulfilled messages are sent to a JMS topic. Both microservices interact with the same JMS provider (message broker) instance, which must therefore scale to support the load of both microservices" Tried to depict scenario in diagram:

To design a caching strategy that queries the database only when there is an update to the Employees table, follow these steps:

On Table Row Operation: Configure an On Table Row operation to monitor the Employees table for updates.

Invalidate Cache: When the On Table Row operation detects an update, trigger the invalidate cache operation to clear the cache.

Object Store Caching Strategy: Use an object-store-caching-strategy to store the cached responses.

Default Expiration Interval: Use the default expiration interval for the cache, which ensures that cached data is invalidated based on the detection of changes in the Employees table rather than a fixed time interval.

This strategy ensures that the cache is only invalidated when there are actual changes in the Employees table, thereby minimizing redundant transactions and optimizing database performance.

References

MuleSoft Documentation on Caching Strategies

Best Practices for Database Integration with MuleSoft

A synchronous invocation of a RESTful API using HTTP to get an individual customer record from a single system exemplifies the request-reply interaction pattern. In this pattern, a client sends a request to a service and waits for a response. The interaction is synchronous because the client expects an immediate reply and is blocked until it receives the response.

This pattern is common in scenarios where the client needs to retrieve specific data or perform an action that requires immediate feedback from the server, such as retrieving a customer record from a database.

References

MuleSoft Integration Patterns Documentation

Request-Reply Pattern Overview

The described approach of gathering all requirements upfront and proceeding through sequential, non-repeating phases is characteristic of the Waterfall methodology. Waterfall is a linear project management approach where each phase must be completed before the next one begins, and there is typically no going back to a previous phase once it is completed. This methodology is well-suited for projects with well-defined requirements and deliverables, but it can be inflexible if changes or new requirements arise during the project.

Explanation

Correct answer is "Compile, package, unit test, validate unit test coverage, deploy" Explanation : Anypoint Platform supports continuous integration and continuous delivery using industry standard tools Mule Maven Plugin The Mule Maven plugin can automate building, packaging and deployment of Mule applications from source projects Using the Mule Maven plugin, you can automate your Mule application deployment to CloudHub, to Anypoint Runtime Fabric, or on-premises, using any of the following deployment strategies • CloudHub deployment • Runtime Fabric deployment • Runtime Manager REST API deployment • Runtime Manager agent deployment MUnit Maven Plugin The MUnit Maven plugin can automate test execution, and ties in with the Mule Maven plugin. It provides a full suite of integration and unit test capabilities, and is fully integrated with Maven and Surefire for integration with your continuous deployment environment. Since MUnit 2.x, the coverage report goal is integrated with the maven reporting section. Coverage Reports are generated during Maven’s site lifecycle, during the coverage-report goal. One of the features of MUnit Coverage is to fail the build if a certain coverage level is not reached. MUnit is not used for integration testing Also publishing to Anypoint Exchange or to create associated API instances in API Manager is not a part of CICD pipeline which can ne achieved using mulesoft provided maven plugin

Explanation

Architecture mentioned in the question can be diagrammatically put as below. Persistent Object Store is the correct answer .

* Mule Object Stores: An object store is a facility for storing objects in or across Mule applications. Mule uses object stores to persist data for eventual retrieval.

Mule provides two types of object stores:

1) In-memory store – stores objects in local Mule runtime memory. Objects are lost on shutdown of the Mule runtime. So we cant use in memory store in our scenario as we want to share watermark within all cloudhub workers

2) Persistent store – Mule persists data when an object store is explicitly configured to be persistent. Hence this watermark will be available even any of the worker goes down

When migrating from an on-premises cluster to a Runtime Fabric (RTF) cluster, and needing to enable Mule applications to store and share data across application replicas and restarts, the use of Anypoint Object Store V2 is the most suitable option. Here’s why and how to implement it:

Understanding Object Store V2:

Object Store V2 is designed to store and retrieve key-value pairs in a scalable and highly available manner. It is particularly useful for sharing state and data between different instances of applications running on RTF.

Setting Up Anypoint Object Store V2:

First, ensure that your MuleSoft Anypoint Platform account has access to Object Store V2.

Configure your Mule application to use Object Store V2 by defining an object store in your Mule configuration file. This can be done using the objectstore element in your Mule flow.

Configuration Steps:

Add the Object Store connector to your Mule project.

Define the object store configuration in your Mule flow as follows:

xml

Use the object store in your flows to store and retrieve data:

xml

Deploying on RTF:

Deploy your Mule application to the RTF cluster. The RTF will handle scaling and ensure that the Object Store V2 is available to all instances of your application.

Benefits:

Object Store V2 ensures data is shared and persisted across different application replicas and restarts, which meets the requirement of storing and sharing data across application replicas in the RTF cluster.

MuleSoft Documentation on Object Store V2

MuleSoft Documentation on Runtime Fabric

For handling millions of requests per day with high performance and reliability, JMS (Java Message Service) transactions are the most suitable method. JMS transactions ensure that messages are processed reliably and can be rolled back in case of failures, providing transactional integrity. This is crucial for a trading company where reliable message processing is critical.

Using JMS transactions within Mule clusters allows for the decoupling of message producers and consumers, improving scalability and fault tolerance. JMS provides built-in mechanisms for handling transactional messaging, ensuring that messages are processed once and only once, even in the event of system failures.

MuleSoft Documentation on JMS

Explanation

Correct answer is VM VM support Transactional Type. When an exception occur, The transaction rolls back to its original state for reprocessing. This feature is not supported by other connectors.

Here is additional information about Transaction management:

Table Description automatically generated

Explanation

Correct answer is For Each scope in the parent flow On Error Continue error handler in the child flow. You can extract below set of requirements from the question a) Records should be sent to downstream system in the same order that it was received in the array b) Any validation or communication failures should not prevent further processing of the remaining records First requirement can be met using For Each scope in the parent flow and second requirement can be met using On Error Continue scope in child flow so that error will be suppressed.

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:

Create Parent POM:

Define a parent POM.xml file that includes all common dependencies, plugin versions, and configurations.

Example of a parent POM.xml:

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

Reference Parent POM:

In each application's POM.xml file, reference the parent POM.

Example of a child POM.xml referencing the parent POM:

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

Build and Test: Ensure that all applications build successfully with the parent POM and test them to confirm that the common dependencies are correctly applied.

Advantages:

Consistency: Ensures all projects use the same versions of dependencies, reducing the risk of conflicts.

Manageability: Simplifies dependency management by centralizing it in one parent POM file.

Scalability: Makes it easier to update dependencies across multiple projects by changing only the parent POM.

References

MuleSoft Documentation on Managing dependencies with Maven

Apache Maven Documentation on POM Reference

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

Anypoint Platform provides the productivity advantage of automatic API proxy generation. This feature enables developers to quickly create proxies for their APIs, which act as intermediaries that forward requests to the appropriate backend services. Automatic proxy generation simplifies the process of securing and managing APIs, allowing developers to enforce policies, monitor traffic, and analyze usage without having to manually configure these aspects.

When a Mule application using VM queues is deployed to a customer-hosted cluster or multiple CloudHub v1.0 workers/replicas, messages are consumed in a non-deterministic way. This means that any of the nodes in the cluster or any of the workers can consume the messages from the VM queues, but there is no guaranteed order or specific pattern (such as round-robin or sequential processing).

This non-deterministic message consumption helps in distributing the load and handling messages more efficiently across multiple nodes or workers, improving the scalability and reliability of the application.

References

MuleSoft Documentation on VM Queues and Clustering

Best Practices for Deploying Mule Applications in Clusters

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

Requirement Analysis: Jenkins files and folders used for CI/CD configurations are not part of the actual Mule package and should be excluded from the deployed archive.

Solution: MuleSoft provides the .muleignore file to specify files and directories that should be excluded from the packaged application.

Implementation Steps:

Create a .muleignore File: In the root directory of your Mule project, create a file named .muleignore.

Specify Exclusions: Add the paths of the Jenkins files and folders that you want to exclude from the deployment package into the .muleignore file.

# .muleignore example /path/to/jenkins/file1 /path/to/jenkins/folder/*

Build and Deploy: When you build your Mule application, the Mule runtime will read the .muleignore file and exclude the specified files and directories from the deployment archive.

Advantages:

Clean Deployment: Ensures that only necessary files are included in the deployment package, reducing the package size and preventing potential issues related to unwanted files.

Ease of Maintenance: Easy to update and manage which files are excluded from the deployment package.

References

MuleSoft Documentation on .muleignore

A Center for Enablement (C4E) provides significant benefits to an enterprise and its lines of business by accelerating self-service capabilities. A C4E is a cross-functional team that promotes the use of best practices, reusable assets, and consistent methodologies across the organization. By enabling lines of business to access and use shared resources, tools, and knowledge independently, a C4E empowers these units to deliver projects faster and more efficiently. This self-service model reduces reliance on central IT for every initiative, fosters innovation, and enhances the overall agility of the organization.

References

MuleSoft Center for Enablement (C4E) Overview

Best Practices for Implementing a C4E on Anypoint Platform

A Center for Enablement (C4E) provides significant benefits by accelerating self-service capabilities for lines of business. Here’s how:

Self-Service Enablement: C4E empowers lines of business to access, use, and create integrations and APIs independently, without relying heavily on central IT teams.

Guidelines and Standards: The C4E establishes best practices, standards, and reusable frameworks, ensuring that lines of business can develop solutions efficiently and consistently.

Faster Time-to-Market: By enabling self-service, lines of business can rapidly develop and deploy solutions to meet their specific needs, resulting in faster time-to-market for new initiatives.

Reduced Bottlenecks: Self-service reduces dependency on central IT, alleviating potential bottlenecks and allowing IT to focus on governance, security, and strategic projects.

The C4E model thus accelerates innovation and responsiveness within the enterprise.

Center for Enablement (C4E)

Benefits of a C4E

Explanation

Every Mule application deployed to CloudHub receives a DNS entry pointing to the CloudHub. The DNS entry is a CNAME for the CloudHub Shared Load Balancer in the region to which the Mule application is deployed. When we deploy the application on CloudHub, we get a generic url to access the endpoints. Generic URL looks as below:

..cloudhub.io is the deployed application name which is unique across all the MuleSoft clients. is the region name in which an application is deployed.

The public CloudHub (shared) load balancer already redirects these requests, where myApp is the name of the Mule application deployment to CloudHub: HTTP requests to http://myApp. .cloudhub.io redirects to

http://mule-worker-myApp. .cloudhub.io:8081

HTTPS traffic to https://myApp. .cloudhub.io redirects to

https://mule-worker-myApp. .cloudhub.io:8082

Anypoint MQ is a cloud-based messaging service provided by MuleSoft, and it cannot be deployed on-premises. If there is a requirement for the messaging broker to be deployed on-premises, Anypoint MQ would not be suitable. Other considerations such as payload format (XML), encryption, and point-to-point messaging are supported by Anypoint MQ, but the deployment environment requirement is a critical constraint.

References

MuleSoft Anypoint MQ Documentation

Cloud vs. On-Premises Messaging Solutions

* Each Batch Job uses SEVERAL THREADS for the Batch Steps

* Each Batch Step instance receives ONE record at a time as the payload. It's not received in a block, as it does not wait for multiple records to be completed before moving to next batch step. (So Option D is out of choice)

* RECORDS are processed IN PARALLEL within and between the two Batch Steps.

* RECORDS are not processed in order. Let's say if second record completes batch_step_1 before record 1, then it moves to batch_step_2 before record 1. (So option C and D are out of choice)

* A batch job is the scope element in an application in which Mule processes a message payload as a batch of records. The term batch job is inclusive of all three phases of processing: Load and Dispatch, Process, and On Complete.

* A batch job instance is an occurrence in a Mule application whenever a Mule flow executes a batch job. Mule creates the batch job instance in the Load and Dispatch phase. Every batch job instance is identified internally using a unique String known as batch job instance id.

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.”

MuleSoft's API development best practices emphasize a design-first approach, which starts with writing and approving an API contract before any implementation begins. This approach ensures that the API's interface is agreed upon and understood by all stakeholders before the backend is built. It involves creating an API specification using tools like RAML or OpenAPI, which serves as a blueprint for development. This method promotes better planning, communication, and alignment between different teams and stakeholders, leading to more efficient and predictable API development processes.

Explanation

Correct answer is Workers are randomly distributed across available AZs within that region. This ensure high availability for deployed mule applications Mulesoft documentation reference :

https://docs.mulesoft.com/runtime-manager/cloudhub-hadr

Customer Relationship Management (CRM) systems are designed to manage a company’s interactions with current and potential customers. They provide capabilities for tracking prospect data, managing customer interactions, and analyzing customer information to improve cross-selling opportunities. By using a CRM system, organizations can consolidate customer information, enhance customer relationships, and leverage data analytics to drive sales and marketing efforts.

Explanation

Correct answer is It is not possible to store logs for 2 years in CloudHub deployment. External log management system is required. CloudHub has a specific log retention policy, as described in the documentation: the platform stores logs of up to 100 MB per app & per worker or for up to 30 days, whichever limit is hit first. Once this limit has been reached, the oldest log information is deleted in chunks and is irretrievably lost. The recommended approach is to persist your logs to a external logging system of your choice (such as Splunk, for instance) using a log appender. Please note that this solution results in the logs no longer being stored on our platform, so any support cases you lodge will require for you to provide the appropriate logs for review and case resolution

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

To track the entire lifecycle of each request across different microservices, the DevOps team should consider adopting tracing. Distributed tracing allows teams to follow a request as it travels through the various microservices, providing visibility into how long each part of the request lifecycle takes and where any bottlenecks or failures occur. Tools like Zipkin, Jaeger, and AWS X-Ray are commonly used for this purpose. Tracing complements metrics and logging by providing detailed insights into the interactions and dependencies between services.

When configuring secure communication with an FTPS server and handling signed files, the necessary components include:

FTPS Server Login Credentials: The username and password used to authenticate with the company's FTPS server.

TLS Context Trust Store: This should contain the public certificate for the FTPS server (ftps.partner.com) to establish a secure SSL/TLS connection.

Partner's PGP Public Key: This is used to verify the signature of the files received from the partner, ensuring data integrity and authenticity.

These components collectively ensure secure communication and data validation.

References

MuleSoft Documentation on FTPS and PGP Encryption

Best Practices for Secure File Transfer and Encryption

Explanation

* Object Store and VM Store is used for sharing data inter or intra mule applications in same setup. Can't be used with external Business Partner

* Also File connector will not be useful as the two companies currently have no shared IT infrastructure. It's specific for local use.

* Correct answer is SFTP connector. The SFTP Connector implements a secure file transport channel so that your Mule application can exchange files with external resources. SFTP uses the SSH security protocol to transfer messages. You can implement the SFTP endpoint as an inbound endpoint with a one-way exchange pattern, or as an outbound endpoint configured for either a one-way or request-response exchange pattern.

Explanation

In Unit testing instead of using actual backends, stubs are used for the backend services. This ensures that developers are not blocked and have no dependency on other systems.

In Unit testing instead of using actual backends, stubs are used for the backend services. This ensures that developers are not blocked and have no dependency on other systems.

Below are the typical characteristics of unit testing.

-- Unit tests do not require deployment into any special environment, such as a staging environment

-- Unit tests san be run from within an embedded Mule runtime

-- Unit tests can/should be implemented using MUnit

-- For read-only interactions to any dependencies (such as other APIs): allowed to invoke production endpoints

-- For write interactions: developers must implement mocks using MUnit

-- Require knowledge of the implementation details of the API implementation under test

Explanation

The API consumer creates an API client which sends API invocations to an API such that they are processed by an API implementation

This is based on below definitions API client • An application component • that accesses a service • by invoking an API of that service - by definition of the term API over HTTP API consumer • A business role, which is often assigned to an individual • that develops API clients, i.e., performs the activities necessary for enabling an API client to invoke APIs API implementation • An application component • that implements the functionality

Digital Transformation Goals:

The company aims to start its digital transformation journey, which typically involves adopting cloud-based solutions to enhance agility, scalability, and innovation.

CloudHub Runtimes:

Fully Managed Service: CloudHub is a fully managed, multi-tenant integration platform as a service (iPaaS) offered by MuleSoft.

Ease of Use: It simplifies deployment and management, requiring minimal infrastructure setup and maintenance from the customer.

Scalability: CloudHub provides seamless scalability to handle varying workloads, which is crucial for growing digital transformation initiatives.

High Availability: CloudHub ensures high availability and reliability through its underlying infrastructure and built-in failover mechanisms.

Security: MuleSoft ensures that CloudHub meets stringent security and compliance standards, which is essential for enterprise applications.

Suitability for Digital Transformation:

Rapid Deployment: CloudHub enables rapid deployment of integrations and APIs, accelerating the digital transformation process.

Cost-Effective: As a managed service, it reduces the total cost of ownership by eliminating the need for extensive infrastructure investments and ongoing operational costs.

Integration Capabilities: CloudHub supports a wide range of integration patterns and connectors, facilitating seamless integration with various systems and data sources.

MuleSoft Documentation on CloudHub: CloudHub

MuleSoft Digital Transformation Insights: MuleSoft Digital Transformation

* "100% increase in the throughput of the API" might look correct, as the number of requests processed per second might increase, but is it guaranteed to increase by 100%? Using 4 nodes will definitely increase throughput of system. But it is cant be precisely said if there would be 100% increase in throughput as it depends on many other factors. Also it is nowhere mentioned in the description that all nodes have same CPU/memory assigned. The question is about the guaranteed behavior * Increasing number of nodes will have no impact on response time as we are scaling application horizontally and not vertically. Similarly there is no change in JVM heap memory usage. * So Correct answer is 50% reduction in the number of requests being received by each node This is because of the two reasons. 1) API is mentioned as stateless 2) Load Balancer is used

SOAP API specifications are provided as WSDL. Design center doesn't provide the functionality to create WSDL file. Hence WSDL needs to be created using XML editor

To improve delivery quality, a MuleSoft integration team should adopt automated testing with MUnit. MUnit is MuleSoft's testing framework that allows developers to create, design, and run unit and integration tests on their Mule applications. Automated testing with MUnit ensures that each part of the Mule application is tested for correctness and performance, catching issues early in the development cycle. This practice leads to higher quality code, reduced defects, and more reliable integrations.

Other practices mentioned, such as continuous design with API Designer and passive monitoring with Anypoint Monitoring, are important but do not directly address the need for rigorous and automated testing to ensure quality.

References

MuleSoft Documentation on MUnit

Best Practices for Automated Testing with MUnit

Explanation

Correct answer is Database polling continues Only HTTP requests sent to the remaining node continue to be accepted. Explanation : Architecture descripted in the question could be described as follows.When node 1 is down , DB polling will still continue via node 2 . Also requests which are coming directly to node 2 will also be accepted and processed in BAU fashion. Only thing that wont work is when requests are sent to Node 1 HTTP connector. The flaw with this architecture is HTTP clients are sending HTTP requests directly to individual cluster nodes. By default, clustering Mule runtime engines ensures high system availability. If a Mule runtime engine node becomes unavailable due to failure or planned downtime, another node in the cluster can assume the workload and continue to process existing events and messages

Diagram Description automatically generated

Explanation

Correct answer is When server-side load-balanced TLS mutual authentication is required between API implementations and API clients CloudHub dedicated load balancers (DLBs) are an optional component of Anypoint Platform that enable you to route external HTTP and HTTPS traffic to multiple Mule applications deployed to CloudHub workers in a Virtual Private Cloud (VPC). Dedicated load balancers enable you to: * Handle load balancing among the different CloudHub workers that run your application. * Define SSL configurations to provide custom certificates and optionally enforce two-way SSL client authentication. * Configure proxy rules that map your applications to custom domains. This enables you to host your applications under a single domain

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.

Requirements:

Asynchronous and Reliable File Processing: The application must process files from an FTPS server to a back-end database using VM intermediary queues for load balancing.

Batch Job Processing: The application must process records from a source to a target system using a Batch Job scope.

Persistent VM Queues:

Reliability: Persistent VM queues ensure that messages are not lost even if there is a system failure or restart. This is critical for reliable file processing and load balancing.

Asynchronous Processing: Persistent queues allow for asynchronous processing, where messages can be stored and processed independently of the producer and consumer lifecycles.

MuleSoft Best Practices:

Persistent VM Queues for Load Balancing: Using persistent VM queues aligns with MuleSoft best practices for ensuring reliable message processing and load balancing between Mule events.

High Availability: With CloudHub 1.0 workers, enabling persistent VM queues helps maintain high availability and reliability of the application.

Batch Job Scope: Ensuring persistence for VM queues also benefits batch processing by maintaining consistency and ensuring all records are processed even in the event of disruptions.

Configuration in Runtime Manager:

Deployment Configuration: When deploying the Mule application in Runtime Manager, check the "Persistent VM queues" checkbox to enable this feature.

Impact: This configuration ensures that the application meets its performance and reliability goals by safeguarding the integrity and continuity of the processing tasks.

MuleSoft Documentation on VM Queues: VM Queues

MuleSoft Best Practices: MuleSoft Best Practices

CloudHub Deployment Guide: CloudHub Deployment

Explanation

Correct answer is Anypoint Runtime Manager

MuleSoft Anypoint Runtime Manager (ARM) provides connectivity to Mule Runtime engines deployed across your organization to provide centralized management, monitoring and analytics reporting. However, most enterprise customers find it necessary for these on-premises runtimes to integrate with their existing non MuleSoft analytics / monitoring systems such as Splunk and ELK to support a single pane of glass view across the infrastructure.

* You can configure the Runtime Manager agent to export data to external analytics tools.

Using either the Runtime Manager cloud console or Anypoint Platform Private Cloud Edition, you can:

--> Send Mule event notifications, including flow executions and exceptions, to Splunk or ELK.

--> Send API Analytics to Splunk or ELK. Sending data to third-party tools is not supported for applications deployed on CloudHub.

You can use the CloudHub custom log appender to integrate with your logging system. Reference: https://docs.mulesoft.com/runtime-manager/ https://docs.mulesoft.com/release-notes/runtime-manager-agent/runtime-manager-agent-release-notes

Additional Info:

It can be achieved in 3 steps:

1) register an agent to a runtime manager,

2) configure a gateway to enable API analytics to be sent to non MuleSoft analytics platform (Splunk for ex.) – as highlighted in the following diagram and

3) setup dashboards.