Salesforce MuleSoft-Platform-Architect-I Exam With Confidence Using Practice Dumps

Explanation

Correct Answer: They must be deployed to Anypoint Platform runtime planes that are managed by Anypoint Platform control planes, with both planes in the same Jurisdiction.

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>> As per legal regulations, all data processing to be performed within a certain jurisdiction. Meaning, the data in USA should reside within USA and should not go out. Same way, the data in EU should reside within EU and should not go out.

>> So, just encrypting the data in transit and at rest does not help to be compliant with the rules. We need to make sure that data does not go out too.

>> The data that we are talking here is not just about the messages that are published to Anypoint MQ. It includes the apps running, transaction states, application logs, events, metric info and any other metadata. So, just replacing Anypoint MQ with a locally hosted ActiveMQ does NOT help.

>> The data that we are talking here is not just about the key/value pairs that are stored in Object Store. It includes the messages published, apps running, transaction states, application logs, events, metric info and any other metadata. So, just avoiding using Object Store does NOT help.

>> The only option left and also the right option in the given choices is to deploy application on runtime and control planes that are both within the jurisdiction.

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.

Explanation

Correct Answer: Model all API resources and methods to closely mimic the operations of the backend system.

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>> There are NO fixed and straight best practices while opting data models for APIs. They are completly contextual and depends on number of factors. Based upon those factors, an enterprise can choose if they have to go with Enterprise Canonical Data Model or Bounded Context Model etc.

>> One should NEVER expose the technical details of API implementation to their API clients. Only the API interface/ RAML is exposed to API clients.

>> It is true that the RAML definitions of APIs should be as detailed as possible and should reflect most of the documentation. However, just that is NOT enough to call your API as best documented API. There should be even more documentation on Anypoint Exchange with API Notebooks etc. to make and create a developer friendly API and repository..

>> The best practice always when creating System APIs is to create their API interfaces by modeling their resources and methods to closely reflect the operations and functionalities of that backend system.