MuleSoft-Integration-Architect-I Exam Dumps - Salesforce MuleSoft Questions and Answers

In the given scenario, a rate limiting policy has been applied to the SOAP API and a global error handler is configured to handle HTTP:RETRY_EXHAUSTED errors with a 429 status code. The rate limiting policy will trigger when the client exceeds the allowed quota of API calls. Since the HTTP:RETRY_EXHAUSTED error specifically catches quota exhaustion errors and the error handler is configured to return a 429 status code, the expected HTTP status returned to the client when the quota is exceeded will be 429. This error code indicates that the user has sent too many requests in a given amount of time ("rate limiting").

MuleSoft Documentation on Error Handling

HTTP Status Codes

To enable the API to securely connect from its CloudHub 2.0 private space to both the on-premises mainframe application and the Kafka cluster in an Amazon AWS VPC, the following approach is recommended:

AWS Transit Gateway: In the AWS account, attach the CloudHub 2.0 private space to an AWS Transit Gateway. This gateway facilitates routing between the CloudHub private space and the on-premises data center.

Routing to On-Premises: The AWS Transit Gateway will route traffic from the CloudHub 2.0 private space to the on-premises data center, enabling secure and efficient communication.

Anypoint VPN: In MuleSoft Runtime Manager, configure an Anypoint VPN to establish a secure connection from the CloudHub 2.0 private space to the AWS VPC where the Kafka cluster is hosted. This VPN ensures encrypted and secure communication between CloudHub and AWS VPC.

This method uses both AWS Transit Gateway and Anypoint VPN to create a secure and efficient network setup, allowing the API to connect to both on-premises and AWS resources.

AWS Transit Gateway Documentation

Anypoint VPN Configuration

Explanation

● Option A says "Perform EACH DB insert in a SEPARATE DB transaction". In this case if first DB insert is successful and second one fails then first insert won't be rolled back causing inconsistency. This option is ruled out.

● Option D says Perform BOTH DB inserts in ONE DB transaction.

Rule of thumb is when one or more DB connections are required we must use XA transaction as local transactions support only one resource. So this option is also ruled out.

● Option B acknowledges the before DB processing, so message is removed from the queue. In case of system failure at later point, message can't be retrieved.

● Option C is Valid: Though it says "do not ack JMS message", message will be auto acknowledged at the end of transaction. Here is how we can ensure all components are part of XA transaction: https://docs.mulesoft.com/jms-connector/1.7/jms-transactions

Additional Information about transactions:

● XA Transactions - You can use an XA transaction to group together a series of operations from multiple transactional resources, such as JMS, VM or JDBC resources, into a single, very reliable, global transaction.

● The XA (eXtended Architecture) standard is an X/Open group standard which specifies the interface between a global transaction manager and local transactional resource managers.

The XA protocol defines a 2-phase commit protocol which can be used to more reliably coordinate and sequence a series of "all or nothing" operations across multiple servers, even servers of different types

● Use JMS ack if

– Acknowledgment should occur eventually, perhaps asynchronously

– The performance of the message receipt is paramount

– The message processing is idempotent

– For the choreography portion of the SAGA pattern

● Use JMS transactions

– For all other times in the integration you want to perform an atomic unit of work

– When the unit of work comprises more than the receipt of a single message

– To simply and unify the programming model (begin/commit/rollback)

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

The data format shown in the exhibit is YAML (YAML Ain't Markup Language). YAML is a human-readable data serialization standard that is commonly used for configuration files and data exchange between languages with different data structures. In the exhibit, the indentation and the use of colons to define key-value pairs are characteristic of YAML.

JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) have different syntax structures, and CSV (Comma-Separated Values) is a flat file format that uses commas to separate values. The format shown in the exhibit fits the structure and style of YAML.

References

YAML Specification Documentation

MuleSoft Documentation on Supported Data Formats

To extend Mule APIs to the EU (Frankfurt) region and minimize latency for European users, follow these steps:

Set Region Property: In Runtime Manager, for each Mule application deployment, set the Region property to EU (Frankfurt). This deploys the application to the desired region, optimizing performance for European users.

Reuse Application Names: Keep the same Mule application names as used in the North American region. This approach maintains consistency and simplifies management.

Communicate New URLs: Inform the consuming API clients in Europe of the new URLs in the format {API-name}—{environment}.de-ci.cloudhub.io. These URLs will direct the clients to the applications deployed in the EU region, ensuring reduced latency and improved performance.

This strategy effectively deploys the same Mule APIs to the CloudHub EU region, leveraging the existing control plane in North America.

The Publish/Subscriber Messaging Bus (Pub/Sub) model is well-suited for high-volume, real-time communication and notification requirements. In this scenario, the retailer needs to notify its order management, warehouse, and billing systems within 15 minutes of order submission. Pub/Sub messaging systems enable asynchronous communication where messages (orders) are published to a topic and multiple subscribers (order management, warehouse, billing systems) receive notifications simultaneously. This decouples the systems and ensures that each subscriber receives the message promptly for subsequent processing.

In an HTTP/1.1-compliant web service, the class of HTTP response status codes that indicates successful client requests is the 2xx class. These status codes signify that the client's request was successfully received, understood, and accepted by the web service. Common 2xx status codes include:

200 OK: The request was successful.

201 Created: The request was successful and a new resource was created.

202 Accepted: The request has been accepted for processing, but the processing is not yet complete.

Other status code classes like 3xx (redirection), 4xx (client errors), and 5xx (server errors) indicate different types of responses and do not signify successful request processing.

References

IETF RFC 2616: HTTP/1.1 Specification

HTTP Status Code Definitions

Explanation

Correct answer is Use a Batch job scope to bulk insert records into the database

* Batch Job is most efficient way to manage millions of records.

A few points to note here are as follows :

Reliability: If you want reliabilty while processing the records, i.e should the processing survive a runtime crash or other unhappy scenarios, and when restarted process all the remaining records, if yes then go for batch as it uses persistent queues.

Error Handling: In Parallel for each an error in a particular route will stop processing the remaining records in that route and in such case you'd need to handle it using on error continue, batch process does not stop during such error instead you can have a step for failures and have a dedicated handling in it.

Memory footprint: Since question said that there are millions of records to process, parallel for each will aggregate all the processed records at the end and can possibly cause Out Of Memory.

Batch job instead provides a BatchResult in the on complete phase where you can get the count of failures and success. For huge file processing if order is not a concern definitely go ahead with Batch Job