Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers
You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible. What should you do?
You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based
customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly
predict an overall satisfaction score from the customer comments on each form. How should you accomplish
this task'?
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?
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