Salesforce MuleSoft Platform Architect I Exam Questions

To accommodate the new requirement of allowing updates to existing quotes, the following actions should be taken:

Update the RAML Definition (Option C):

The RAML specification defines the structure and behavior of the API. Adding a new method (such as PUT or PATCH) for updating quotes requires modifying the RAML to include this new endpoint. This ensures the API specification is up-to-date and accurately reflects the new functionality.

Update the API Implementation (Option A):

Once the RAML is updated, the backend API implementation must also be modified to handle the new update requests. This could involve adding logic to process and validate update requests, connect to necessary backend resources, and apply the changes to existing quotes.

of Incorrect Options:

Option B (removing and creating new clients) is unnecessary; client applications can remain as they are, with no need for complete replacement.

Option D (deprecating existing versions) may not be required if backward compatibility is maintained.

Option E (adding a new policy) does not facilitate functional changes and is unrelated to implementing the update feature.

Reference For more details on updating RAML definitions and API implementations, refer to MuleSoft's API Design documentation on RAML and RESTful API practices.

Correct Answer : TRUE

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>> As per MuleSoft proposed IT Operating Model, designing APIs and making sure that they are discoverable and self-servable is VERY VERY IMPORTANT and decides the success of an API and its application network.

Correct Answer : Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

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The scenario in the question is very clearly stating that the usual traffic in the year is pretty well handled by the existing worker configuration with CPU running well below 70%. The problem occurs only 'sometimes' occasionally when there is spike in the number of orders coming in.

So, based on above, We neither need to permanently increase the size of each worker nor need to permanently increase the number of workers. This is unnecessary as other than those 'occasional' times the resources are idle and wasted.

We have two options left now. Either to use horizontal Cloudhub autoscaling policy to automatically increase the number of workers or to use vertical Cloudhub autoscaling policy to automatically increase the vCore size of each worker.

Here, we need to take two things into consideration:

1. CPU

2. Order Submission Rate to JMS Queue

>> From CPU perspective, both the options (horizontal and vertical scaling) solves the issue. Both helps to bring down the usage below 90%.

>> However, If we go with Vertical Scaling, then from Order Submission Rate perspective, as the application is still being load balanced with two workers only, there may not be much improvement in the incoming request processing rate and order submission rate to JMS queue. The throughput would be same as before. Only CPU utilization comes down.

>> But, if we go with Horizontal Scaling, it will spawn new workers and adds extra hand to increase the throughput as more workers are being load balanced now. This way we can address both CPU and Order Submission rate.

Hence, Horizontal CloudHub Autoscaling policy is the right and best answer.

Correct Answer : When development is already organized into several independent initiatives or groups

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>> It would require lot of process effort in an organization to have a single C4E team coordinating with multiple already organized development teams which are into several independent initiatives. A single C4E works well with different teams having at least a common initiative. So, in this scenario, federated C4E works well instead of centralized C4E.