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Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem. After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders. You load the Orders table into an Apache Spark DataFrame named df. You need to create a DataFrame that excludes rows where the order amount is null. Solution: You run the following expression. df.fillna(0, subset=['order_amount']) Does this meet the goal?
Correct Answer: B
Correct: * You run the following expression. df.dropna(subset=["order_amount"]) The expression df.dropna(subset=["order_amount"]) is an appropriate and effective way to exclude rows where order_amount is null. * You run the following expression. df.filter(df.order_amount.isNotNull()) To exclude rows where the order amount is null, you can use the isNotNull() method or a SQL expression within the filter() or where() functions.Here are the standard, appropriate expressions: Option 1: Python/PySpark API (Recommended) pythondf_clean = df.filter(df["order_amount"].isNotNull()) Incorrect: * You run the following expression. df.fillna(0, subset=['order_amount']) * You run the following expression. df.filter(df.order_amount != None) Reference: https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/ https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna
