Google Cloud Professional Machine Learning Engineer — Question 336

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

Answer options

• A. Use the Natural Language API to classify support requests.
• B. Use AutoML Natural Language to build the support requests classifier.
• C. Use an established text classification model on AI Platform to perform transfer learning.
• D. Use an established text classification model on AI Platform as-is to classify support requests.

Correct answer: C

Explanation

The correct answer is C because using an established text classification model on AI Platform for transfer learning allows you to leverage pre-trained capabilities and adapt the model to your specific needs. Options A and B suggest using APIs or AutoML, which do not provide the same level of control over the model as TensorFlow does. Option D would not allow for customization or improvement of the model, as it uses the model without modification.