Databricks Certified Data Engineer - Professional
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In a real-world scenario, you are working with a large dataset in PySpark that includes a column named 'date' formatted as 'yyyy-MM-dd' in string type. Your task involves converting this column to a date type to facilitate various date-related operations such as filtering records by a specific month or calculating the difference between dates. Considering the need for efficiency, accuracy, and the specific requirement to perform operations that only involve the date part (without time), which of the following approaches is the BEST and why? Choose the single best option.
A
Use the to_date() function to convert the 'date' column to a date type and then perform the date-related operations. This approach is straightforward and directly addresses the requirement by converting the string to a date type, enabling all necessary date operations without unnecessary complexity._
B
Use the to_timestamp() function to convert the 'date' column to a timestamp type and then perform the date-related operations. While this approach works, it introduces unnecessary time component information that is not required for the task, potentially complicating the operations and consuming more resources._
C
Use the to_date() function to convert the 'date' column to a date type, and then use the cast() function to convert it to a timestamp type before performing the date-related operations. This approach adds an unnecessary step of converting to a timestamp type, which does not align with the requirement of working solely with the date part._
D
Use the cast() function to convert the 'date' column directly to a timestamp type and then perform the date-related operations. This approach skips the conversion to a date type, which might not be suitable for all date-related operations, especially those that require operations on the date part only.