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Google Professional-Machine-Learning-Engineer Dumps Questions Answers

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Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?

Choose 2 answers



Use the interleave option for reading data


Reduce the value of the repeat parameter


Increase the buffer size for the shuffle option.


Set the prefetch option equal to the training batch size


Decrease the batch size argument in your transformation

Questions 2

You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?



Create a new view with BigQuery that does not include a column with city information


Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.


Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.


Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

Questions 3

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?



Use the Natural Language API to classify support requests


Use AutoML Natural Language to build the support requests classifier


Use an established text classification model on Al Platform to perform transfer learning


Use an established text classification model on Al Platform as-is to classify support requests

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