Salesforce MuleSoft-Platform-Architect-I Dumps Questions Answers

Explanation

Correct Answer: Process Layer

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>> Experience layer is dedicated for enrichment of end user experience. This layer is to meet the needs of different API clients/ consumers.

>> System layer is dedicated to APIs which are modular in nature and implement/ expose various individual functionalities of backend systems

>> Process layer is the place where simple or complex business orchestration logic is written by invoking one or many System layer modular APIs

So, Process Layer is the right answer.

In MuleSoft’s ecosystem, when a Process API needs to consume a System API (published to Exchange and protected by OAuth 2.0), the recommended approach is to utilize the REST Connect Connector. Here’s how it aligns with best practices:

Automated Connector Generation:

When a RAML or OAS specification is published in Exchange, MuleSoft automatically generates a REST Connect Connector for that API. This connector simplifies integration as it abstracts the complexity of making HTTP requests and handling OAuth authentication.

Streamlined Integration:

The Process API can import this generated connector from Exchange and configure OAuth credentials, streamlining secure access to the System API without manual HTTP setup.

Why Option C is Correct:

Using the REST Connect Connector directly leverages MuleSoft’s automated tooling, minimizing manual configurations and ensuring a more maintainable integration.

Explanation of Incorrect Options:

Option A (importing an OAuth module) is unnecessary; OAuth is handled within the connector’s configuration.

Option B (property YAML files with HTTP requests) involves manual setup, which is more error-prone and not recommended.

Option D (manually updating POM file) does not directly aid in invoking an API through Exchange.

ReferencesFor more information on using REST Connect Connectors and OAuth integration in MuleSoft, refer to the MuleSoft documentation on API Management and Connectors​​.

Explanation

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

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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.