Mulesoft MCPA-Level-1 Exam Questions

An API has been updated in Anypoint Exchange by its API producer from version 3.1.1 to
3.2.0 following accepted semantic versioning practices and the changes have been
communicated via the API's public portal.
The API endpoint does NOT change in the new version.
How should the developer of an API client respond to this change?

A.

The update should be identified as a project risk and full regression testing of the functionality that uses this API should be run

B.

The API producer should be contacted to understand the change to existing functionality

C.

The API producer should be requested to run the old version in parallel with the new one

D.

The API client code ONLY needs to be changed if it needs to take advantage of new
features

D.

The API client code ONLY needs to be changed if it needs to take advantage of new
features

Reference: https://docs.mulesoft.com/exchange/to-change-raml-version

When could the API data model of a System API reasonably mimic the data model
exposed by the corresponding backend system, with minimal improvements over the
backend system's data model?

A.

When there is an existing Enterprise Data Model widely used across the organization

B.

When the System API can be assigned to a bounded context with a corresponding data
model

C.

When a pragmatic approach with only limited isolation from the backend system is deemed appropriate

D.

When the corresponding backend system is expected to be replaced in the near future

C.

When a pragmatic approach with only limited isolation from the backend system is deemed appropriate

Explanation: Explanation
Correct Answer: When a pragmatic approach with only limited isolation from the backend
system is deemed appropriate.
*****************************************
General guidance w.r.t choosing Data Models:
>> If an Enterprise Data Model is in use then the API data model of System APIs should
make use of data types from that Enterprise Data Model and the corresponding API
implementation should translate between these data types from the Enterprise Data Model
and the native data model of the backend system.
>> If no Enterprise Data Model is in use then each System API should be assigned to a
Bounded Context, the API data model of System APIs should make use of data types from
the corresponding Bounded Context Data Model and the corresponding API
implementation should translate between these data types from the Bounded Context Data
Model and the native data model of the backend system. In this scenario, the data types in
the Bounded Context Data Model are defined purely in terms of their business
characteristics and are typically not related to the native data model of the backend system.
In other words, the translation effort may be significant.
>> If no Enterprise Data Model is in use, and the definition of a clean Bounded Context
Data Model is considered too much effort, then the API data model of System APIs should
make use of data types that approximately mirror those from the backend system, same
semantics and naming as backend system, lightly sanitized, expose all fields needed for
the given System API’s functionality, but not significantly more and making good use of
REST conventions.
The latter approach, i.e., exposing in System APIs an API data model that basically mirrors
that of the backend system, does not provide satisfactory isolation from backend systems
through the System API tier on its own. In particular, it will typically not be possible to
"swap out" a backend system without significantly changing all System APIs in front of that
backend system and therefore the API implementations of all Process APIs that depend on
those System APIs! This is so because it is not desirable to prolong the life of a previous
backend system’s data model in the form of the API data model of System APIs that now
front a new backend system. The API data models of System APIs following this approach
must therefore change when the backend system is replaced.
On the other hand:
>> It is a very pragmatic approach that adds comparatively little overhead over accessing
the backend system directly
>> Isolates API clients from intricacies of the backend system outside the data model
(protocol, authentication, connection pooling, network address, …)
>> Allows the usual API policies to be applied to System APIs
>> Makes the API data model for interacting with the backend system explicit and visible,
by exposing it in the RAML definitions of the System APIs
>> Further isolation from the backend system data model does occur in the API

A business process is being implemented within an organization's application network. The architecture group proposes using a more coarse-grained application network design with relatively fewer APIs deployed to the application network compared to a more fine-grained design. Overall, which factor typically increases with a more coarse-grained design for this business process implementation and deployment compared with using a more finegrained design?

A. The complexity of each API implementation

B. The number of discoverable assets related to APIs deployed in the application network

C. The number of possible connections between API implementations in the application network

D. The usage of network infrastructure resources by the application network

A. The complexity of each API implementation

The application network is recomposable: it is built for change because it "bends but does
not break"

A.

TRUE

B.

FALSE

A.

TRUE

Explanation: *****************************************
>> Application Network is a disposable architecture.
>> Which means, it can be altered without disturbing entire architecture and its
components.
>> It bends as per requirements or design changes but does not break
Reference: https://www.mulesoft.com/resources/api/what-is-an-application-network

Refer to the exhibit.

What is the best way to decompose one end-to-end business process into a collaboration of Experience, Process, and System APIs?
A) Handle customizations for the end-user application at the Process API level rather than the Experience API level
B) Allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs
C) Always use a tiered approach by creating exactly one API for each of the 3 layers (Experience, Process and System APIs)
D) Use a Process API to orchestrate calls to multiple System APIs, but NOT to other Process APIs

A. Option A

B. Option B

C. Option C

D. Option D

B. Option B

Explanation:
Correct Answer: Allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs.

All customizations for the end-user application should be handled in "Experience API" only. Not in Process API
We should use tiered approach but NOT always by creating exactly one API for each of the 3 layers. Experience APIs might be one but Process APIs and System APIs are often more than one. System APIs for sure will be more than one all the time as they are the smallest modular APIs built in front of end systems.
Process APIs can call System APIs as well as other Process APIs. There is no such anti-design pattern in API-Led connectivity saying Process APIs should not call other Process APIs.

So, the right answer in the given set of options that makes sense as per API-Led connectivity principles is to allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs. This way, some future Process APIs can make use of that data from System APIs and we need NOT touch the System layer APIs again and again.

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:

What are the major benefits of MuleSoft proposed IT Operating Model?

A.

1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Focus on creation of reusable assets first. Upon finishing creation of all the possible
assets then inform the LOBs in the organization to start using them

B.

1. Decrease the IT delivery gap
2. Meet various business demands by increasing the IT capacity and forming various IT
departments
3. Make consumption of assets at the rate of production

C.

1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production

C.

1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production

Explanation: Explanation
Correct Answer:
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production.
*****************************************
Reference: https://www.youtube.com/watch?v=U0FpYMnMjmM

A retail company with thousands of stores has an API to receive data about purchases and
insert it into a single database. Each individual store sends a batch of purchase data to the
API about every 30 minutes. The API implementation uses a database bulk insert
command to submit all the purchase data to a database using a custom JDBC driver
provided by a data analytics solution provider. The API implementation is deployed to a
single CloudHub worker. The JDBC driver processes the data into a set of several
temporary disk files on the CloudHub worker, and then the data is sent to an analytics
engine using a proprietary protocol. This process usually takes less than a few minutes.
Sometimes a request fails. In this case, the logs show a message from the JDBC driver
indicating an out-of-file-space message. When the request is resubmitted, it is successful.
What is the best way to try to resolve this throughput issue?

A.

se a CloudHub autoscaling policy to add CloudHub workers

B.

Use a CloudHub autoscaling policy to increase the size of the CloudHub worker

C.

Increase the size of the CloudHub worker(s)

D.

Increase the number of CloudHub workers

D.

Increase the number of CloudHub workers

Explanation: Explanation
Correct Answer: Increase the size of the CloudHub worker(s)
*****************************************
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.