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Question 89 - Professional Machine Learning Engineer discussion

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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?

A.
Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
Answers
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.
Answers
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.
Answers
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.
Answers
D.
Create a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.
Suggested answer: B

Explanation:

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.

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asked 18/09/2024
Robin Koele
34 questions
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