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

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?
B) Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
A) Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
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.

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?

Show Suggested Answer Hide 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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Chan
26 minutes ago
C is interesting, but I feel it's more complex than needed.
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Paz
5 days ago
A seems quick, but I doubt its accuracy.
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Aretha
10 days ago
I prefer D. BERT is powerful for text classification.
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Geraldo
16 days ago
I think B is the best option. AutoML is user-friendly.
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Charlena
21 days ago
C seems outdated, why not use the latest tech like D?
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Truman
26 days ago
Wait, can BigQuery ML really handle this well? Sounds too simple.
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Dottie
2 months ago
A is too basic for this task, we need something more robust.
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Cyril
3 months ago
I think D could give better accuracy with BERT.
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Dorsey
3 months ago
B is the way to go! AutoML makes it easy.
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Colette
3 months ago
C sounds interesting, but is logistic regression enough?
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Serina
3 months ago
Wait, can AutoML really handle all that?
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Felix
3 months ago
A seems too basic for this task.
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Carmen
3 months ago
I think D could give better accuracy with BERT.
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Evelynn
4 months ago
B is the best option for quick deployment!
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Madelyn
4 months ago
I recall that BigQuery ML is great for quick models, but I wonder if logistic regression is powerful enough for classifying text like this.
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Dorothy
4 months ago
I practiced a similar question where we had to choose between different ML tools. I feel like the Cloud Natural Language API might not give us enough customization for our needs.
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Dominque
4 months ago
I'm not entirely sure, but I remember something about using BERT for text classification. It seems like a strong option, but it might be more complex to set up.
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Lawrence
4 months 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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