Salesforce MuleSoft-Platform-Architect-I Dumps - Pass Salesforce Certified MuleSoft Platform Architect Exam in 2026

Correct Answe r: They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

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In API-led connectivity,

>> Experience APIs - allow for innovation at the user interface level by consuming the underlying assets without being aware of how data is being extracted from backend systems.

>> Process APIs - compose data from various sources and combine them with orchestration logic to create higher level value

>> System APIs - reduce the dependency on the underlying backend systems by helping unlock data from backend systems in a reusable and consumable way.

However, they NEVER promise that they provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

https://dzone.com/articles/api-led-connectivity-with-mule

When a Basic Authentication policy is applied to an API on CloudHub but clients can still access the API without authentication, the likely cause is a missing Autodiscovery element. Here's how this affects API security:

Autodiscovery in MuleSoft:

The Autodiscovery element is essential for linking an API implementation deployed in CloudHub with its API instance defined in API Manager. This connection allows the policies applied in API Manager, such as Basic Authentication, to be enforced on the deployed API.

Why Option B is Correct:

Without Autodiscovery, the deployed application does not 'know' about the policies configured in API Manager, resulting in unrestricted access. Adding Autodiscovery enables the API to enforce the policies correctly.

of Incorrect Options:

Option A (incorrect Exchange version) would not cause bypassing of security policies.

Option C (missing client applications) does not impact authentication policy enforcement.

Option D (worker restart) is irrelevant to policy enforcement.

Reference Refer to MuleSoft documentation on Autodiscovery configuration and linking API Manager policies for additional information on setting up secure API policies.

Correct Answe r:Create reusable assets, Make them discoverable so that LOB teams can self-serve and browse the APIs, Get active feedback and usage metrics.

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Correct Answe r: 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 Answe r: Increase the size of the CloudHub worker(s)

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The key details that we can take out from the given scenario are:

>> API implementation uses a database bulk insert command to submit all the purchase data to a database

>> JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker

>> Sometimes a request fails and the logs show a message indicating an out-of-file-space message

Based on above details:

>> Both auto-scaling options does NOT help because we cannot set auto-scaling rules based on error messages. Auto-scaling rules are kicked-off based on CPU/Memory usages and not due to some given error or disk space issues.

>> Increasing the number of CloudHub workers also does NOT help here because the reason for the failure is not due to performance aspects w.r.t CPU or Memory. It is due to disk-space.

>> Moreover, the API is doing bulk insert to submit the received batch data. Which means, all data is handled by ONE worker only at a time. So, the disk space issue should be tackled on 'per worker' basis. Having multiple workers does not help as the batch may still fail on any worker when disk is out of space on that particular worker.

Therefore, the right way to deal this issue and resolve this is to increase the vCore size of the worker so that a new worker with more disk space will be provisioned.