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Salesforce Certified MuleSoft Platform Architect Sample Questions (Q90-Q95):

NEW QUESTION # 90
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?

Answer: C

Explanation:
Correct Answe r: When a pragmatic approach with only limited isolation from the backend system is deemed appropriate.
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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 implementations of the Process API tier


NEW QUESTION # 91
A set of tests must be performed prior to deploying API implementations to a staging environment. Due to data security and access restrictions, untested APIs cannot be granted access to the backend systems, so instead mocked data must be used for these tests. The amount of available mocked data and its contents is sufficient to entirely test the API implementations with no active connections to the backend systems. What type of tests should be used to incorporate this mocked data?

Answer: C

Explanation:
Correct Answe r: Unit tests (Whitebox)
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Reference:
As per general IT testing practice and MuleSoft recommended practice, Integration and Performance tests should be done on full end to end setup for right evaluation. Which means all end systems should be connected while doing the tests. So, these options are OUT and we are left with Unit Tests and Functional Tests.
As per attached reference documentation from MuleSoft:
Unit Tests - are limited to the code that can be realistically exercised without the need to run it inside Mule itself. So good candidates are Small pieces of modular code, Sub Flows, Custom transformers, Custom components, Custom expression evaluators etc.
Functional Tests - are those that most extensively exercise your application configuration. In these tests, you have the freedom and tools for simulating happy and unhappy paths. You also have the possibility to create stubs for target services and make them success or fail to easily simulate happy and unhappy paths respectively.
As the scenario in the question demands for API implementation to be tested before deployment to Staging and also clearly indicates that there is enough/ sufficient amount of mock data to test the various components of API implementations with no active connections to the backend systems, Unit Tests are the one to be used to incorporate this mocked data.


NEW QUESTION # 92
An organization is deploying their new implementation of the OrderStatus System API to multiple workers in CloudHub. This API fronts the organization's on-premises Order Management System, which is accessed by the API implementation over an IPsec tunnel.
What type of error typically does NOT result in a service outage of the OrderStatus System API?

Answer: D

Explanation:
Correct Answe r: A CloudHub worker fails with an out-of-memory exception.
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>> An AWS Region itself going down will definitely result in an outage as it does not matter how many workers are assigned to the Mule App as all of those in that region will go down. This is a complete downtime and outage.
>> Extended outage of API manager during initial deployment of API implementation will of course cause issues in proper application startup itself as the API Autodiscovery might fail or API policy templates and polices may not be downloaded to embed at the time of applicaiton startup etc... there are many reasons that could cause issues.
>> A network outage onpremises would of course cause the Order Management System not accessible and it does not matter how many workers are assigned to the app they all will fail and cause outage for sure.
The only option that does NOT result in a service outage is if a cloudhub worker fails with an out-of-memory exception. Even if a worker fails and goes down, there are still other workers to handle the requests and keep the API UP and Running. So, this is the right answer.


NEW QUESTION # 93
An organization wants to create a Center for Enablement (C4E). The IT director schedules a series of meetings with IT senior managers.
What should be on the agenda of the first meeting?

Answer: A

Explanation:
In the initial meeting for establishing a Center for Enablement (C4E), it's essential to lay the foundational vision, objectives, and guiding principles for the team. Here's why this is crucial:
Clear Vision and Mission:
Defining the mission statement and objectives at the start ensures alignment within the organization and clarifies the C4E's role in supporting API-led development and integration practices.
Guiding Principles:
Establishing guiding principles will help the C4E maintain consistent practices and strategies across projects. This serves as a framework for decisions and fosters shared understanding among IT leaders and stakeholders.
of Correct Answer (A):
By prioritizing the C4E's objectives and mission, the organization builds a solid foundation, paving the way for subsequent meetings focused on technical standards, processes, and operating models.
of Incorrect Options:
Option B (API monetization) and Option C (common services best practices) are specific topics better suited for later discussions.
Option D (specifying the operating model) is an important step but typically follows the establishment of the C4E's objectives and vision.
Reference
For more on C4E objectives and foundational setup, refer to MuleSoft's documentation on establishing a C4E and the roles and mission statements recommended for such initiatives.


NEW QUESTION # 94
An organization has built an application network following the API-led connectivity approach recommended by MuleSoft. To protect the application network against attacks from malicious external API clients, the organization plans to apply JSON Threat Protection policies.
To which API-led connectivity layer should the JSON Threat Protection policies most commonly be applied?

Answer: C

Explanation:
Understanding JSON Threat Protection Policies:
JSON Threat Protection policies are used to protect APIs from attacks that exploit JSON payloads, such as oversized payloads, deeply nested objects, and excessive array elements. This helps prevent Denial of Service (DoS) attacks and other malicious payload-related threats.
These policies are typically applied to safeguard APIs that are directly exposed to external clients, where the risk of receiving malicious payloads is highest.
API-led Connectivity Layers:
Experience Layer: This layer is designed to expose APIs to end-users or external API clients, often acting as the interface that interacts with users or applications.
Process Layer: This layer is used for orchestration and aggregation of data from various System APIs, typically operating within a trusted environment and not directly exposed to external clients.
System Layer: This layer provides access to backend systems and databases, often within the organization's secure environment and not directly accessible to external clients.
Evaluating the Options:
Option A (All layers): While JSON Threat Protection can technically be applied to all layers, it is most commonly applied at the Experience layer, where APIs are exposed to external traffic and are more vulnerable to attacks.
Option B (System layer): The System layer is generally not exposed to external clients directly, so JSON Threat Protection is less critical here.
Option C (Process layer): Similar to the System layer, the Process layer is typically internal and not exposed directly to external clients, so JSON Threat Protection is less commonly applied.
Option D (Correct Answer): The Experience layer is the correct answer because it is the layer that directly interacts with external clients, making it the primary target for malicious payloads. Applying JSON Threat Protection here effectively protects the application network from external threats.
Conclusion:
Option D is the correct answer, as the Experience layer is the most common layer for applying JSON Threat Protection policies to protect against external attacks.
For further reference, consult MuleSoft's documentation on API security policies and best practices for securing APIs at the Experience layer.


NEW QUESTION # 95
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