Pass the MuleSoft MuleSoft Certified Architect MCIA-Level-1 Questions and answers with CertsForce

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

Data confidentiality involves protecting information from unauthorized access and disclosure. Encrypting a file containing personally identifiable information (PII) is a prime example of ensuring data confidentiality. Encryption transforms the data into a format that is unreadable without the appropriate decryption key, thereby safeguarding sensitive information such as PII from being accessed by unauthorized parties. This measure is essential for compliance with data protection regulations and maintaining the privacy and security of personal data.

References

MuleSoft Security Best Practices

Data Protection and Encryption Standards Documentation

According to the MuleSoft Catalyst framework, an Integration Architect plays a crucial role in defining and achieving business outcomes. One of their key responsibilities is to agree upon Key Performance Indicators (KPIs) and help develop an overall success plan. This involves working with stakeholders to identify measurable goals and ensure that the integration initiatives align with the organization’s strategic objectives.

KPIs are critical for tracking progress, measuring success, and making data-driven decisions. By agreeing on KPIs and developing a success plan, the Integration Architect ensures that the organization can objectively measure the impact of its integration efforts and adjust strategies as needed to achieve desired business outcomes.

References:

MuleSoft Catalyst Knowledge Hub

* Sharing Mule applications as templates is a great way to share your work with other people who are in your organization in Anypoint Platform. When they need to build a similar application they can create the mule application using the template project from Anypoint studio.

* Anypoint Templates are designed to make it easier and faster to go from a blank canvas to a production application. They’re bit for bit Mule applications requiring only Anypoint Studio to build and design, and are deployable both on-premises and in the cloud.

* Anypoint Templates are based on five common data Integration patterns and can be customized and extended to fit your integration needs. So even if your use case involves different endpoints or connectors than those included in the template, they still offer a great starting point. 

Some of the best practices while creating the template project: - Define the common error handler as part of template project, either using pom dependency or mule config file - Define common logger/audit framework as part of the template project - Define the env specific properties and secure properties file as per the requirement - Define global.xml for global configuration - Define the config file for connector configuration like Http,Salesforce,File,FTP etc - Create separate folders to create DWL,Properties,SSL certificates etc - Add the dependency and configure the pom.xml as per the business need - Configure the mule-artifact.json as per the business need

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

Explanation

* Option B shouldn't be used unless extremely needed, if RAML is changed, client needs to accommodate changes. Question is about minimizing impact on Client. So this is not a valid choice.

* Option C isn't valid as Business can't stop for consumers acknowledgment.

* Option D again needs Client to accommodate changes and isn't viable option.

* Best choice is A where RAML definition isn't changed and underlined functionality is changed without any dependency on client and without impacting client.

* "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

Scatter-Gather sends a request message to multiple targets concurrently. It collects the responses from all routes, and aggregates them into a single message.