Which endpoints should the Enrichment Cloud Functions call?

You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.

The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?
A . 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
B . 1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API
C . 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
D . 1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API

Answer: B

Explanation:

https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture

The architecture has the following flow:

A user writes a ticket to Firebase, which triggers a Cloud Function.

-The Cloud Function calls 3 different endpoints to enrich the ticket:

-An AI Platform endpoint, where the function can predict the priority.

-An AI Platform endpoint, where the function can predict the resolution time.

-The Natural Language API to do sentiment analysis and word salience.

-For each reply, the Cloud Function updates the Firebase real-time database.

-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.