Google Professional Machine Learning Engineer Exam - Topic 4 Question 112 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 112
Topic #: 4

[All Professional Machine Learning Engineer Questions]

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?

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 BigQuery ML to perform inference.

DCreate a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

Show Suggested Answer

Suggested Answer: B

AutoML Natural Language is a service that allows you to quickly build, test and deploy natural language processing (NLP) models without needing to have expertise in NLP or machine learning. You can use it to train a classifier on your corpus of written support cases, and then use the AutoML API to perform classification on new requests. Once the model is trained, it can be deployed as a REST API. This allows the classifier to be integrated into your pipeline and be easily consumed by other systems.

by Rory at Mar 06, 2026, 08:17 PM

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1 month ago

I think using AutoML Natural Language could be a good choice since it simplifies the process of building a classifier.

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