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Exam Certified Machine Learning Associate All Questions

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Exam Certified Machine Learning Associate topic 1 question 18 discussion

Actual exam question from Databricks's Certified Machine Learning Associate
Question #: 18
Topic #: 1
[All Certified Machine Learning Associate Questions]

A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

  • A. predict(*spark_df.columns)
  • B. mapInPandas(predict)
  • C. predict(Iterator(spark_df))
  • D. mapInPandas(predict(spark_df.columns))
  • E. predict(spark_df.columns)
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Suggested Answer: A 🗳️
by souarav at June 1, 2024, 2:35 p.m.

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souarav
Highly Voted 5 months, 3 weeks ago
Selected Answer: A
mapInPandas is used in pandas api functions and syntax is mapInPandas(predict,schema)
upvoted 5 times
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ricorosol
Most Recent 1 month, 3 weeks ago
B. mapInPandas(predict): This is the correct choice. mapInPandas is used to apply a Pandas UDF to a Spark DataFrame. This function expects the UDF to take an iterator of Pandas DataFrames and return an iterator of Pandas Series or DataFrames, which matches the signature of the predict function defined.
upvoted 1 times
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rajneesharora
4 months, 3 weeks ago
correct answer is A, Scalar Pandas UDFs work with column names or expressions and return a column that gets added to the DataFrame. In this particular case, the use of *spark_df.columns unpacks the column names, which allows the UDF to operate on all these columns. No other option provides all column names
upvoted 1 times
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