Assume you have a DataFrame df loaded from a CSV file located at '/data/sales.csv' with columns 'id', 'product', 'quantity', and 'price'. You need to create a temporary view named 'sales_view' that can be used in subsequent SQL queries. How would you achieve this using PySpark?

Simulated

Explanation:

The correct answer is A because it correctly uses the 'createOrReplaceTempView' method to create a temporary view named 'sales_view' that can be used in subsequent SQL queries. This method ensures that the view is always up-to-date with the DataFrame.

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Assume you have a DataFrame df loaded from a CSV file located at '/data/sales.csv' with columns 'id', 'product', 'quantity', and 'price'. You need to create a temporary view named 'sales_view' that can be used in subsequent SQL queries. How would you achieve this using PySpark?