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?

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

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A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

• A.Convert the records to Apache Parquet format.
• B.Convert the records to JSON format.
• C.Convert the records to GZIP CSV format.
• D.Convert the records to XML format.

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You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

• A.Downsample the data with upweighting to create a sample with 10% positive examples
• B.Use a convolutional neural network with max pooling and softmax activation
• C.Remove negative examples until the numbers of positive and negative examples are equal
• D.Use the class distribution to generate 10% positive examples

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You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?

• A.Write a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source.
• B.Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.
• C.Write a query that preprocesses the data by using BigQuery Export the query results as CSV files and use those files to create a Vertex Al managed dataset.
• D.Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset.

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You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery.
The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

• A.Create a custom TensorFlow DNN model.
• B.Use BQML XGBoost regression to train the model
• C.Use AutoML Tables to train the model without early stopping.
• D.Use AutoML Tables to train the model with RMSLE as the optimization objective

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