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?

Question

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:

User writes a Helpdesk
support ticket Platform
Create Ticket
Firebase
Cloud Functions
| Offline
Enrichment Endpoint 1 |, | | Classification
Training
Cloud Functions
‘ Regression
Endpoint2 |||
pein Training

(Experience,
category, type,
impact, seniority)

Endpoint 3

Which endpoints should the Enrichment Cloud Functions call?

Exam-Answer · Accepted Answer

1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language

Question 12 of 56 from exam PMLE: Professional Machine Learning Engineer

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