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The following code has been migrated to a Databricks notebook from a legacy workload: The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data. Which statement is a possible explanation for this behavior?
Correct Answer: E
Explanation https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.
