Mulesoft MCPA-Level-1 Exam Questions

151 Questions


Updation Date : 1-Dec-2025



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Which three tools automate the deployment of Mule applications? (Choose 3 answers)


A. Runtime Manager


B. Anypoint Platform CLI


C. Platform APIs


D. Anypoint Studio


E. Mule Mayen plugin


F. API Community Manager





A.
  Runtime Manager

B.
  Anypoint Platform CLI

C.
  Platform APIs

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:
References:
For detailed steps on automating deployments with these tools, refer to MuleSoft documentation on Runtime Manager, CLI, and Platform APIs.

An API with multiple API implementations (Mule applications) is deployed to both CloudHub and customer-hosted Mule runtimes. All the deployments are managed by the MuleSoft-hosted control plane. An alert needs to be triggered whenever an API implementation stops responding to API requests, even if no API clients have called the API implementation for some time. What is the most effective out-of-the-box solution to create these alerts to monitor the API implementations?


A. Create monitors in Anypoint Functional Monitoring for the API implementations, where each monitor repeatedly invokes an API implementation endpoint


B. Add code to each API client to send an Anypoint Platform REST API request to generate a custom alert in Anypoint Platform when an API invocation times out


C. Handle API invocation exceptions within the calling API client and raise an alert from that API client when such an exception is thrown


D. Configure one Worker Not Responding alert.in Anypoint Runtime Manager for all API implementations that will then monitor every API implementation





A.
  Create monitors in Anypoint Functional Monitoring for the API implementations, where each monitor repeatedly invokes an API implementation endpoint

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:
References:
For further information, refer to MuleSoft documentation on Anypoint Functional Monitoring setup and usage for API availability monitoring.

What condition requires using a CloudHub Dedicated Load Balancer?


A.

When cross-region load balancing is required between separate deployments of the same Mule application


B.

When custom DNS names are required for API implementations deployed to customerhosted Mule runtimes


C.

When API invocations across multiple CloudHub workers must be load balanced


D.

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





D.
  

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
*****************************************
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 must an API implementation be deployed to an Anypoint VPC?


A.

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


B.

When the API implementation must be accessible within a subnet of a restricted customer-hosted network that does not allow public access


C.

When the API implementation must be deployed to a production AWS VPC using the Mule Maven plugin


D.

When the API Implementation must write to a persistent Object Store





A.
  

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



An application updates an inventory running only one process at any given time to keep the inventory consistent. This process takes 200 milliseconds (.2 seconds) to execute; therefore, the scalability threshold of the application is five requests per second. What is the impact on the application if horizontal scaling is applied, thereby increasing the number of Mule workers?


A. The application scalability threshold is five requests per second regardless of the horizontal scaling


B. The total process execution time is now 100 milliseconds (.1 seconds)


C. The application scalability threshold is now 10 requests per second


D. Horizontal scaling cannot be applied to an already-running application





A.
  The application scalability threshold is five requests per second regardless of the horizontal scaling

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:

A Mule application exposes an HTTPS endpoint and is deployed to the CloudHub Shared Worker Cloud. All traffic to that Mule application must stay inside the AWS VPC. To what TCP port do API invocations to that Mule application need to be sent?


A. 443


B. 8081


C. 8091


D. 8082





D.
  8082

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
So, API invocations should be sent to port 8082 when calling this HTTPS based app.

A retail company is using an Order API to accept new orders. The Order API uses a JMS
queue to submit orders to a backend order management service. The normal load for
orders is being handled using two (2) CloudHub workers, each configured with 0.2 vCore.
The CPU load of each CloudHub worker normally runs well below 70%. However, several
times during the year the Order API gets four times (4x) the average number of orders.
This causes the CloudHub worker CPU load to exceed 90% and the order submission time
to exceed 30 seconds. The cause, however, is NOT the backend order management
service, which still responds fast enough to meet the response SLA for the Order API.
What is the MOST resource-efficient way to configure the Mule application's CloudHub
deployment to help the company cope with this performance challenge?


A.

Permanently increase the size of each of the two (2) CloudHub workers by at least four
times (4x) to one (1) vCore


B.

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


C.

Permanently increase the number of CloudHub workers by four times (4x) to eight (8)
CloudHub workers


D.

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





D.
  

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%
*****************************************
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.

A company stores financial transaction data in two legacy systems. For each legacy system, a separate, dedicated System API (SAPI) exposes data for that legacy system. A Process API (PAPI) merges the data retrieved from ail of the System APIs into a common format. Several API clients call the PAPI through its public domain name.
The company now wants to expose a subset of financial data to a newly developed mobile application that uses a different Bounded Context Data Model. The company wants to follow MuleSoft's best practices for building out an effective application network.
Following MuleSoft's best practices, how can the company expose financial data needed by the mobile application in a way that minimizes the impact on the currently running API clients, API implementations, and support asset reuse?


A. Add two new Experience APIs (EAPI-i and EAPI-2}.
Add Mobile PAPI-2 to expose the Intended subset of financial data as requested.
Both PAPIs access the Legacy Systems via SAPI-1 and SAP]-2.


B. Add two new Experience APIs (EAPI-i and EAPI-2}.
Add Mobile PAPI-2 to expose the Intended subset of financial data as requested.
Both PAPIs access the Legacy Systems via SAPI-1 and SAP]-2.


C. Create a new mobile Experince API (EAPI) chat exposes that subset of PAPI endpoints.
Add transformtion login to the mobile Experince API implementation to make mobile data compatible with the required PAPIs.


D. Develop and deploy is new PAPI implementation with data transformation and ... login to support this required endpoints of both mobile and web clients.
Deploy an API Proxy with an endpoint from API Manager that redirect the existing PAPI endpoints to the new PAPI.





A.
  Add two new Experience APIs (EAPI-i and EAPI-2}.
Add Mobile PAPI-2 to expose the Intended subset of financial data as requested.
Both PAPIs access the Legacy Systems via SAPI-1 and SAP]-2.

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.


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