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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #78
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a s
The correct answer is B. Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.. AutoML is easier and faster and "you need to quickly build, test, and deploy". Also the REST API part fits our use case.
Submitted by kevin_r· Apr 18, 2026ML pipeline operationalization
Question
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
Options
- AUse the Cloud Natural Language API to obtain metadata to classify the incoming cases.
- BUse AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
- CUse BigQuery ML to build and test a logistic regression model to classify incoming requests. Use
- DCreate a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier,
How the community answered
(32 responses)- A3% (1)
- B84% (27)
- C9% (3)
- D3% (1)
Explanation
AutoML is easier and faster and "you need to quickly build, test, and deploy". Also the REST API part fits our use case.
Topics
#Text Classification#AutoML Natural Language#Model Deployment#NLP
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