Note that private flow has error scope defined as On Error Continue . So when error occurs in private flow , it is handled by this On Error Continue scope which sends success response back to main flow and does not throw back an error. So main continues normally and payload is set to Success - main flow.
Hence correct answer is Success - main flow
1) HTTP listener received request
2) The Flow Reference calls the child flow
3) The Is Number validator creates an Error Object because the payload isn't an integer. Child Flow execution stops
#[error.description] = "payload is not a valid INTEGER value"
#[error.errorType] = VALIDATION:INVALID_NUMBER
4) The On Error Continue handles the errorThe payload is set to "Error - Sub Flow"
5) "Error - Sub Flow" is returned to the main flow as if the child flow was a success. The Set Payload is executed. The payload is reset to "Success - Finished Main Flow"
6) "Success - Main Flow" is returned to the requestor in the body of the HTTP request. HTTP Status Code: 200 As you can see, in the above example, because the error was caught by an On Error Continue scope in the child flow (RED in, GREEN out) when the Mule Message returns to the parent flow, the parent flow knows none-the-different that there was a failure because the on error continue returns a 200 success message. Note that because, to the mainFlow, the childFlow appeared to succeed, the processing of mainFlow resumed after the flow reference.

* RAML file contains lot of information that could be considered as "not API-describing". Sort of "economy-class" members.
Equally important, but not necessarily part of the main RAML file.
* Through !includes, RAML allows us to build file-distributed API definitions, which is not only useful to encourage code reuse but also improves readability.
* We can create RAML fragments with such code and then include them in main RAML project using
!include like:
types:
Book: !include bookDataType.raml and
examples:
input: !include bookExample.raml
* Additionally for

Correct answer is Mule runtime
DataWeave is the MuleSoft expression language for accessing and transforming data that travels through a Mule app. DataWeave is tightly integrated with the Mule runtime engine, which runs the scripts and expressions in your Mule app.
Mule Doc Reference : https://docs.mulesoft.com/mule-runtime/4.3/dataweave