Google Cloud Professional Machine Learning Engineer — Question 243

You work at an ecommerce startup. You need to create a customer churn prediction model. Your company’s recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?

Answer options

• A. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
• B. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
• C. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.
• D. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.

Correct answer: C

Explanation

The correct option is C because it allows you to efficiently manage your data within BigQuery and leverage AutoML capabilities, which can speed up the model training process while minimizing costs. Option A involves exporting data unnecessarily, and option B suggests a more complex deep learning approach without the quick iteration that AutoML provides. Option D also unnecessarily exports data to Cloud Storage and focuses on deep learning, which may not be as efficient for initial model development.