A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

• A.The data will be limited to a single executor preventing the model from being loaded multiple times
• B.The model will be limited to a single executor preventing the data from being distributed
• C.The model only needs to be loaded once per executor rather than once per batch during the inference process
• D.The data will be distributed across multiple executors during the inference process

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A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?

• A.RMSE
• B.Precision
• C.Area under the residual operating curve
• D.Accuracy
• E.Recall

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A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

• A.They can refactor their notebook to process the data in parallel.
• B.They can refactor their notebook to use the PySpark DataFrame API.
• C.They can refactor their notebook to use the Scala Dataset API.
• D.They can refactor their notebook to use Spark SQL.
• E.They can refactor their notebook to utilize the pandas API on Spark.

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Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

• A.The vectorized pandas UDFs allow for the use of type hints
• B.The vectorized pandas UDFs process data in batches rather than one row at a time
• C.The vectorized pandas UDFs allow for pandas API use inside of the function
• D.The vectorized pandas UDFs work on distributed DataFrames
• E.The vectorized pandas UDFs process data in memory rather than spilling to disk

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Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

• A.pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
• B.pandas API on Spark DataFrames are more performant than Spark DataFrames
• C.pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
• D.pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

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