Mulesoft MCPA-Level-1 Exam Questions

Explanation:
MuleSoft offers various tools to automate the deployment of Mule applications, which can streamline deployment and management processes. Here’s how each tool supports automated deployment:

• Runtime Manager:
• Anypoint Platform CLI:
• Platform APIs:
• Explanation of Incorrect Options:

Explanation:
In scenarios where multiple API implementations are deployed across different environments (CloudHub and customer-hosted runtimes), Anypoint Functional Monitoring is the most effective tool to monitor API availability and trigger alerts when an API implementation becomes unresponsive. Here’s how it works:

• Using Anypoint Functional Monitoring:
• Why Option A is Correct:
• Explanation of Incorrect Options:

When server-side load-balanced TLS mutual authentication is required between API
implementations and API clients

Explanation: Explanation
Correct Answer: When server-side load-balanced TLS mutual authentication is required
between API implementations and API clients
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Fact/ Memory Tip: Although there are many benefits of CloudHub Dedicated Load
balancer, TWO important things that should come to ones mind for considering it are:
>> Having URL endpoints with Custom DNS names on CloudHub deployed apps
>> Configuring custom certificates for both HTTPS and Two-way (Mutual) authentication.
Coming to the options provided for this question:
>> We CANNOT use DLB to perform cross-region load balancing between separate
deployments of the same Mule application.
>> We can have mapping rules to have more than one DLB URL pointing to same Mule
app. But vicevera (More than one Mule app having same DLB URL) is NOT POSSIBLE
>> It is true that DLB helps to setup custom DNS names for Cloudhub deployed Mule apps
but NOT true for apps deployed to Customer-hosted Mule Runtimes.
>> It is true to that we can load balance API invocations across multiple CloudHub workers
using DLB but it is NOT A MUST. We can achieve the same (load balancing) using SLB
(Shared Load Balancer) too. We DO NOT necessarily require DLB for achieve it.
So the only right option that fits the scenario and requires us to use DLB is when TLS
mutual authentication is required between API implementations and API clients.
Reference: https://docs.mulesoft.com/runtime-manager/cloudhub-dedicated-load-balancer

When the API Implementation must invoke publicly exposed services that are deployed outside of CloudHub in a customer- managed AWS instance

Explanation:
Given that the application is designed to handle only one process at a time to maintain data consistency, here’s why horizontal scaling won’t increase the processing limit:
Single-Process Constraint:

• Execution Time:
• Explanation of Correct Answer (A):
• Explanation of Incorrect Options:

Explanation:
Correct Answer: 8082

• 8091 and 8092 ports are to be used when keeping your HTTP and HTTPS app private to the LOCAL VPC respectively.
• Above TWO ports are not for Shared AWS VPC/ Shared Worker Cloud.
• 8081 is to be used when exposing your HTTP endpoint app to the internet through Shared LB
• 8082 is to be used when exposing your HTTPS endpoint app to the internet through Shared LB

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

Explanation: Explanation
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.

Explanation:
To achieve the goal of exposing financial data to a new mobile application while following MuleSoft’s best practices, the company should follow an API-led connectivity approach.
This approach ensures minimal disruption to existing clients, maximizes reusability, and respects the separation of concerns across API layers.
Explanation of Solution:
Experience APIs for Client-Specific Requirements:
Process API Layer for Data Transformation:
Reuse of System APIs:
Why Option A is Correct:
Explanation of Incorrect Options:
Option B: This option seems similar but lacks clarity on the separation of mobilespecific requirements and does not explicitly mention data transformation, which is essential in this scenario.
Option C: Creating a single mobile Experience API that exposes a subset of PAPI endpoints directly adds unnecessary complexity and may violate the separation of concerns, as transformation logic should not be in the Experience layer.
Option D: Deploying a new PAPI and using an API Proxy to redirect existing endpoints would add unnecessary complexity, disrupt the current API clients, and increase maintenance efforts.
References:
For additional guidance, refer to MuleSoft documentation on API-led connectivity best practices and best practices for structuring Experience, Process, and System APIs.