Google Cloud Professional Machine Learning Engineer — Question 63

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?

Answer options

• 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.

Correct answer: B

Explanation

The correct answer is B because AutoML Natural Language provides a streamlined way to build, test, and deploy custom machine learning models specifically for text classification tasks, which is ideal for this scenario. Option A does not create a classifier, option C focuses on a regression model rather than classification, and option D involves a more complex TensorFlow setup that may not be as efficient for rapid deployment.