MuleSoft-Platform-Architect-I Dumps with Practice Exam Questions Answers

Understanding Control and Runtime Planes:

Control Plane: The control plane is responsible for managing, monitoring, and deploying Mule applications. In a Private Cloud Edition (PCE), this control plane is deployed on-premises within the customer’s infrastructure, meeting data residency and security requirements.

Runtime Plane: The runtime plane consists of Mule runtimes that execute Mule applications. By hosting these runtimes within the customer’s infrastructure, data and metadata can remain local, which complies with corporate policies regarding data residency.

Evaluating the Options:

Option A: Using Anypoint Platform Private Cloud Edition for the control plane and the MuleSoft-hosted runtime plane would not meet the requirement, as the runtime plane is hosted by MuleSoft and would not keep data local.

Option B: The MuleSoft-hosted control plane with Anypoint Runtime Fabric for the runtime plane would still mean that metadata is managed in MuleSoft’s cloud, which does not comply with the requirement to keep data and metadata on-premises.

Option C: A MuleSoft-hosted control plane and customer-hosted Mule runtimes also mean that metadata resides in the cloud, not on-premises, failing the residency requirement.

Option D (Correct Answer): Anypoint Platform Private Cloud Edition (PCE) for the control plane and customer-hosted Mule runtimes fulfill both requirements, as both the control plane and runtime plane would be hosted within the customer’s data center.

Conclusion:

Option D is the correct answer, as it ensures that both the control plane and runtime plane are hosted on-premises, allowing data and metadata to reside locally per the corporate IT policy.

Refer to MuleSoft's documentation on Private Cloud Edition deployment and on-premise runtime configurations for further details.

Explanation

Correct Answer: 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

Explanation

Correct Answer: JSON threat protection

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Fact: Technically, there are no restrictions on what policy can be applied in what layer. Any policy can be applied on any layer API. However, context should also be considered properly before blindly applying the policies on APIs.

That is why, this question asked for a policy that would LEAST likely be applied to a Process API.

From the given options:

>> All policies except "JSON threat protection" can be applied without hesitation to the APIs in Process tier.

>> JSON threat protection policy ideally fits for experience APIs to prevent suspicious JSON payload coming from external API clients. This covers more of a security aspect by trying to avoid possibly malicious and harmful JSON payloads from external clients calling experience APIs.

As external API clients are NEVER allowed to call Process APIs directly and also these kind of malicious and harmful JSON payloads are always stopped at experience API layer only using this policy, it is LEAST LIKELY that this same policy is again applied on Process Layer API.

Monitoring On-Premise Mule Applications:

For Mule applications deployed on an on-premises cluster, monitoring detailed performance metrics requires communication with the MuleSoft-hosted control plane. The control plane, when used with on-premises runtimes, relies on Anypoint Monitoring and requires a Monitoring Agent to gather and send detailed performance metrics.

Setting Up Monitoring:

To enable detailed metrics, the Monitoring Agent must be installed on each node in the cluster where Mule applications are deployed. This agent collects data on memory usage, CPU load, response times, and other metrics, and sends it to the control plane for aggregation and visualization.

Evaluating the Options:

Option A: Updating settings in the control plane alone does not enable detailed monitoring; the agent must be installed on each node to capture detailed metrics.

Option B (Correct Answer): Installing the Monitoring Agent on each node ensures that each runtime node in the cluster can send its metrics to the control plane, enabling detailed monitoring.

Option C: Installing the agent on a separate server would not be effective, as each node in the cluster needs to independently report its metrics to ensure full visibility.

Option D: The on-prem runtime does not automatically send detailed metrics to the control plane without the Monitoring Agent installed.

Conclusion:

Option B is the correct answer, as installing the Monitoring Agent on each node is essential for detailed performance monitoring of on-prem applications in a cluster.

Refer to MuleSoft’s documentation on configuring Anypoint Monitoring for on-premises deployments and using the Monitoring Agent.

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 Runtime Manager is MuleSoft’s web-based interface that allows users to deploy, manage, and monitor applications directly. It provides deployment automation through its user-friendly interface.

Anypoint Platform CLI:

The Anypoint CLI enables scripting of deployment and management tasks, making it possible to automate deployments via command-line scripts. This tool is ideal for CI/CD pipelines as it integrates with automated processes.

Platform APIs:

MuleSoft’s Platform APIs allow programmatic access to deployment functions, enabling integration with external automation tools and CI/CD systems. These APIs facilitate deployment through RESTful calls, which can be automated for continuous delivery.

Explanation of Incorrect Options:

Option D (Anypoint Studio) is primarily for development and does not support deployment automation.

Option E (Maven Plugin) can be used for building and deploying Mule applications but isn’t classified as a platform tool for deployment.

Option F (API Community Manager) is unrelated to deployment and instead focuses on managing API communities.

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