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Exam Certified Data Engineer Professional topic 1 question 105 discussion

Actual exam question from Databricks's Certified Data Engineer Professional
Question #: 105
Topic #: 1
[All Certified Data Engineer Professional Questions]

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"?

  • A. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
  • B. df.select("customer_id", model(*columns).alias("predictions"))
  • C. model.predict(df, columns)
  • D. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
  • E. df.apply(model, columns).select("customer_id, predictions")
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Suggested Answer: B 🗳️
by 60ties at Nov. 15, 2023, 5:30 p.m.

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aragorn_brego
Highly Voted 11 months, 2 weeks ago
Selected Answer: B
This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias("predictions") is used to name the output column of the model's predictions. This will result in a DataFrame with the desired schema: "customer_id LONG, predictions DOUBLE".
upvoted 7 times
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divingbell17
Highly Voted 10 months ago
Selected Answer: B
B is correct. It's a spark udf not pandas
upvoted 6 times
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60ties
Most Recent 11 months, 3 weeks ago
I think it is B
upvoted 2 times
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