CERTIFIED-DATA-ENGINEER-PROFESSIONAL Question #104: Real Exam Question with Answer & Explanation

Question

The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE. The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model. Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

Options

• Amodel.predict(df, columns)
• Bdf.map(lambda x:model(x[columns])).select("customer_id, predictions")
• Cdf.select("customer_id", model(*columns).alias("predictions"))
• Ddf.apply(model, columns).select("customer_id, predictions")
• Edf.select("customer_id", pandas_udf(model, columns).alias("predictions"))