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

• A.Category_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))
• B.Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))
• C.Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))
• D.Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))

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A Delta Live Table pipeline includes two datasets defined using streaming live table. Three datasets are defined against Delta Lake table sources using live table.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?

• A.All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
• B.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
• C.All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
• D.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

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

Calculate the total sales amount for each product category and store the results in a new dataframe called category_sales.
What will generate the expected result of category_sales?

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

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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.agg(sum("sales_amount").groupBy("region").alias("total sales amount"))
• B.region_sales = sales_df. sum ("salen_aiTiount") . groupBy ("region") .alias ("total_sale3_amount")
• C.region_sales = sales_df.groupBy("region").agg(sum("sales_amountM).alias("total_sales_amount"))
• D.region_sales= sales_df.groupBy("category").sum(nsales_amount").alias("t_otal_sales_amounl")

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A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

• A.Option A
• B.Option B
• C.Option C
• D.Option D
• E.Option E

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