A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

• A.They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
• B.They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
• C.They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
• D.There is no way to determine why a Job task is running slowly.
• E.They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.

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A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

• A.The table was managed
• B.The table's data was smaller than 10 GB
• C.The table's data was larger than 10 GB
• D.The table was external
• E.The table did not have a location

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A company sells products across multiple categories (e.g., Electronics, Clothing) and regions.
The sales team has provided you with a PySpark dataframe named sales_df as below, and the team wants the data engineer to analyze the sales data to help make strategic decisions.

Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:

Which code will generate the expected result?

• A.region_sales = sales_df.groupBy("category").sum("sales_amount").alias("total_sales_amount")
• B.region_sales = sales_df.groupBy("region").agg(sum("sales_amount").alias("total_sales_amount"))
• C.region_sales = sales_df.sum("sales_amount").groupBy("region").alias("total_sales_amount")
• D.region_sales = sales_df.agg(sum("sales_amount").groupBy("region").alias("total_sales_amount"))

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