Google Professional Machine Learning Engineer
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Imagine you are managing a support system with a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. Your goal is to quickly build, test, and deploy an automated service that will classify future written requests into one of these categories. Considering the need for fast development and deployment, how should you configure the machine learning pipeline?
A
Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
B
Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
C
Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.
D
Create a TensorFlow model using Google’s BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.